Overview

Dataset statistics

Number of variables92
Number of observations750862
Missing cells16362479
Missing cells (%)23.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 GiB
Average record size in memory4.2 KiB

Variable types

CAT71
NUM17
BOOL2
UNSUPPORTED1
DATE1

Warnings

Incident Address has a high cardinality: 175243 distinct values High cardinality
Type of Incident has a high cardinality: 1112 distinct values High cardinality
Modus Operandi (MO) has a high cardinality: 426497 distinct values High cardinality
Victim Injury Description has a high cardinality: 23046 distinct values High cardinality
Incident Number w/year has a high cardinality: 700639 distinct values High cardinality
Call (911) Problem has a high cardinality: 117 distinct values High cardinality
Type Location has a high cardinality: 73 distinct values High cardinality
Apartment Number has a high cardinality: 11491 distinct values High cardinality
Victim Home Address has a high cardinality: 272530 distinct values High cardinality
Victim Apartment has a high cardinality: 11828 distinct values High cardinality
Victim City has a high cardinality: 6439 distinct values High cardinality
Victim State has a high cardinality: 82 distinct values High cardinality
Victim Business Name has a high cardinality: 15910 distinct values High cardinality
Victim Business Address has a high cardinality: 13471 distinct values High cardinality
Victim Business Phone has a high cardinality: 7238 distinct values High cardinality
Responding Officer #1 Badge No has a high cardinality: 4304 distinct values High cardinality
Responding Officer #1 Name has a high cardinality: 4252 distinct values High cardinality
Time1 of Occurrence has a high cardinality: 1440 distinct values High cardinality
Responding Officer #2 Badge No has a high cardinality: 4289 distinct values High cardinality
Responding Officer #2 Name has a high cardinality: 4274 distinct values High cardinality
Reporting Officer Badge No has a high cardinality: 4330 distinct values High cardinality
Assisting Officer Badge No has a high cardinality: 2648 distinct values High cardinality
Reviewing Officer Badge No has a high cardinality: 180 distinct values High cardinality
Element Number Assigned has a high cardinality: 4221 distinct values High cardinality
Time2 of Occurrence has a high cardinality: 1440 distinct values High cardinality
Investigating Unit 2 has a high cardinality: 62 distinct values High cardinality
Offense Entered Time has a high cardinality: 1440 distinct values High cardinality
CFS Number has a high cardinality: 644806 distinct values High cardinality
RMS Code has a high cardinality: 1298 distinct values High cardinality
Penal Code has a high cardinality: 603 distinct values High cardinality
UCR Offense Name has a high cardinality: 53 distinct values High cardinality
Victim Name has a high cardinality: 411413 distinct values High cardinality
NIBRS Crime has a high cardinality: 55 distinct values High cardinality
Victim Zip Code has a high cardinality: 8252 distinct values High cardinality
City has a high cardinality: 128 distinct values High cardinality
Location1 has a high cardinality: 181464 distinct values High cardinality
Sector is highly correlated with BeatHigh correlation
Beat is highly correlated with SectorHigh correlation
Year1 of Occurrence is highly correlated with Year of Incident and 2 other fieldsHigh correlation
Year of Incident is highly correlated with Year1 of Occurrence and 2 other fieldsHigh correlation
Year2 of Occurrence is highly correlated with Year of Incident and 2 other fieldsHigh correlation
Day2 of the Year is highly correlated with Day1 of the Year and 1 other fieldsHigh correlation
Day1 of the Year is highly correlated with Day2 of the Year and 1 other fieldsHigh correlation
Offense Entered Year is highly correlated with Year of Incident and 2 other fieldsHigh correlation
Offense Entered Date/Time is highly correlated with Day1 of the Year and 1 other fieldsHigh correlation
Victim Age at Offense is highly correlated with Victim AgeHigh correlation
Victim Age is highly correlated with Victim Age at OffenseHigh correlation
Month2 of Occurence is highly correlated with Month1 of Occurence and 1 other fieldsHigh correlation
Month1 of Occurence is highly correlated with Month2 of Occurence and 1 other fieldsHigh correlation
Offense Entered Month is highly correlated with Month1 of Occurence and 1 other fieldsHigh correlation
Offense Type is highly correlated with UCR Offense Name and 3 other fieldsHigh correlation
UCR Offense Name is highly correlated with Offense Type and 2 other fieldsHigh correlation
UCR Offense Description is highly correlated with Offense Type and 2 other fieldsHigh correlation
NIBRS Crime is highly correlated with Offense Type and 4 other fieldsHigh correlation
NIBRS Crime Category is highly correlated with Offense Type and 4 other fieldsHigh correlation
NIBRS Crime Against is highly correlated with UCR Offense Name and 4 other fieldsHigh correlation
NIBRS Code is highly correlated with NIBRS Crime and 3 other fieldsHigh correlation
NIBRS Group is highly correlated with UCR Offense Name and 5 other fieldsHigh correlation
NIBRS Type is highly correlated with NIBRS GroupHigh correlation
Modus Operandi (MO) has 50502 (6.7%) missing values Missing
Victim Condition has 727655 (96.9%) missing values Missing
Victim Injury Description has 717389 (95.5%) missing values Missing
Victim Gender has 280269 (37.3%) missing values Missing
Person Involvement Type has 27637 (3.7%) missing values Missing
Call (911) Problem has 28075 (3.7%) missing values Missing
Type of Property has 580428 (77.3%) missing values Missing
Victim Type has 35768 (4.8%) missing values Missing
Apartment Number has 587135 (78.2%) missing values Missing
Victim Race has 277119 (36.9%) missing values Missing
Reporting Area has 18639 (2.5%) missing values Missing
Victim Home Address has 44510 (5.9%) missing values Missing
Victim Apartment has 545670 (72.7%) missing values Missing
Victim City has 45112 (6.0%) missing values Missing
Council District has 273214 (36.4%) missing values Missing
Victim State has 50304 (6.7%) missing values Missing
Target Area Action Grids has 511352 (68.1%) missing values Missing
Victim Business Name has 714181 (95.1%) missing values Missing
Community has 670559 (89.3%) missing values Missing
Victim Business Address has 724416 (96.5%) missing values Missing
Victim Business Phone has 740352 (98.6%) missing values Missing
Responding Officer #1 Badge No has 30186 (4.0%) missing values Missing
Responding Officer #1 Name has 31010 (4.1%) missing values Missing
Responding Officer #2 Badge No has 479138 (63.8%) missing values Missing
Responding Officer #2 Name has 479139 (63.8%) missing values Missing
Reporting Officer Badge No has 28126 (3.7%) missing values Missing
Assisting Officer Badge No has 194218 (25.9%) missing values Missing
Element Number Assigned has 29185 (3.9%) missing values Missing
Investigating Unit 1 has 206592 (27.5%) missing values Missing
Investigating Unit 2 has 206581 (27.5%) missing values Missing
Offense Status has 9960 (1.3%) missing values Missing
UCR Disposition has 9904 (1.3%) missing values Missing
Family Offense has 27379 (3.6%) missing values Missing
Hate Crime has 750045 (99.9%) missing values Missing
Weapon Used has 353483 (47.1%) missing values Missing
Gang Related Offense has 324256 (43.2%) missing values Missing
Victim Package has 750862 (100.0%) missing values Missing
CFS Number has 28074 (3.7%) missing values Missing
Drug Related Istevencident has 27392 (3.6%) missing values Missing
UCR Offense Name has 355587 (47.4%) missing values Missing
Special Report (Pre-RMS) has 750425 (99.9%) missing values Missing
UCR Offense Description has 355587 (47.4%) missing values Missing
UCR Code has 355587 (47.4%) missing values Missing
Victim Name has 30862 (4.1%) missing values Missing
Offense Type has 355587 (47.4%) missing values Missing
NIBRS Crime has 249799 (33.3%) missing values Missing
NIBRS Crime Category has 249799 (33.3%) missing values Missing
Victim Ethnicity has 277767 (37.0%) missing values Missing
NIBRS Crime Against has 249799 (33.3%) missing values Missing
Victim Age has 312625 (41.6%) missing values Missing
NIBRS Code has 249799 (33.3%) missing values Missing
Victim Age at Offense has 331921 (44.2%) missing values Missing
NIBRS Group has 249799 (33.3%) missing values Missing
NIBRS Type has 249799 (33.3%) missing values Missing
Victim Zip Code has 52198 (7.0%) missing values Missing
X Coordinate has 19655 (2.6%) missing values Missing
Y Cordinate has 19655 (2.6%) missing values Missing
State has 8971 (1.2%) missing values Missing
Zip Code is highly skewed (γ1 = -265.7452969) Skewed
Incident Number w/year is uniformly distributed Uniform
CFS Number is uniformly distributed Uniform
Service Number ID has unique values Unique
Victim Package is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2021-03-12 22:19:50.695522
Analysis finished2021-03-12 22:39:08.943886
Duration19 minutes and 18.25 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Incident Address
Categorical

HIGH CARDINALITY

Distinct175243
Distinct (%)23.4%
Missing3176
Missing (%)0.4%
Memory size5.7 MiB
1400 S LAMAR ST
 
3549
8687 N CENTRAL EXPY
 
1806
8008 HERB KELLEHER WAY
 
1578
1600 FUN WAY
 
1062
725 N JIM MILLER RD
 
1052
Other values (175238)
738639 
ValueCountFrequency (%) 
1400 S LAMAR ST35490.5%
 
8687 N CENTRAL EXPY18060.2%
 
8008 HERB KELLEHER WAY15780.2%
 
1600 FUN WAY10620.1%
 
725 N JIM MILLER RD10520.1%
 
8687 N CENTRAL SERV SB10120.1%
 
9915 E NORTHWEST HWY10110.1%
 
9301 FOREST LN9050.1%
 
1521 N COCKRELL HILL RD8720.1%
 
7401 SAMUELL BLVD8480.1%
 
3550 E OVERTON RD8050.1%
 
205 S LAMAR ST7970.1%
 
1999 E CAMP WISDOM RD7790.1%
 
200 SHORT BLVD7550.1%
 
7425 BONNIE VIEW RD7460.1%
 
13350 DALLAS PKWY7290.1%
 
9801 HARRY HINES BLVD7240.1%
 
1818 CORSICANA ST6860.1%
 
6185 RETAIL RD6530.1%
 
4230 W ILLINOIS AVE6430.1%
 
9350 SKILLMAN ST6090.1%
 
2417 N HASKELL AVE5890.1%
 
2755 E LEDBETTER DR5450.1%
 
5333 W KIEST BLVD5400.1%
 
334 S HALL ST5390.1%
 
Other values (175218)72385296.4%
 
(Missing)31760.4%
 
2021-03-12T16:39:09.941660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique97615 ?
Unique (%)13.1%
2021-03-12T16:39:10.134107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length53
Median length17
Mean length17.17105407
Min length1

Overview of Unicode Properties

Unique unicode characters47
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
191703314.9%
 
R7972346.2%
 
E7853866.1%
 
A6490945.0%
 
06271394.9%
 
L6197874.8%
 
N5817464.5%
 
D5219844.0%
 
S5106284.0%
 
14980363.9%
 
T4823793.7%
 
O4244143.3%
 
23630192.8%
 
I3455742.7%
 
33174252.5%
 
52768442.1%
 
V2611262.0%
 
42346811.8%
 
W2302691.8%
 
M2195491.7%
 
C2173551.7%
 
H2066141.6%
 
71894231.5%
 
B1864561.4%
 
91841291.4%
 
Other values (22)12457689.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter791189261.4%
 
Decimal Number305370523.7%
 
Space Separator191703314.9%
 
Lowercase Letter95280.1%
 
Other Punctuation846< 0.1%
 
Dash Punctuation88< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
062713920.5%
 
149803616.3%
 
236301911.9%
 
331742510.4%
 
52768449.1%
 
42346817.7%
 
71894236.2%
 
91841296.0%
 
81815895.9%
 
61814205.9%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1917033100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R79723410.1%
 
E7853869.9%
 
A6490948.2%
 
L6197877.8%
 
N5817467.4%
 
D5219846.6%
 
S5106286.5%
 
T4823796.1%
 
O4244145.4%
 
I3455744.4%
 
V2611263.3%
 
W2302692.9%
 
M2195492.8%
 
C2173552.7%
 
H2066142.6%
 
B1864562.4%
 
Y1702482.2%
 
P1430951.8%
 
K1373291.7%
 
G1293301.6%
 
U1134421.4%
 
F1108131.4%
 
J342640.4%
 
X225890.3%
 
Z94570.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n635266.7%
 
a317633.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-88100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
&45954.3%
 
.32738.7%
 
/374.4%
 
,91.1%
 
#91.1%
 
'30.4%
 
;20.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin792142061.4%
 
Common497167238.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
191703338.6%
 
062713912.6%
 
149803610.0%
 
23630197.3%
 
33174256.4%
 
52768445.6%
 
42346814.7%
 
71894233.8%
 
91841293.7%
 
81815893.7%
 
61814203.6%
 
&459< 0.1%
 
.327< 0.1%
 
-88< 0.1%
 
/37< 0.1%
 
,9< 0.1%
 
#9< 0.1%
 
'3< 0.1%
 
;2< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R79723410.1%
 
E7853869.9%
 
A6490948.2%
 
L6197877.8%
 
N5817467.3%
 
D5219846.6%
 
S5106286.4%
 
T4823796.1%
 
O4244145.4%
 
I3455744.4%
 
V2611263.3%
 
W2302692.9%
 
M2195492.8%
 
C2173552.7%
 
H2066142.6%
 
B1864562.4%
 
Y1702482.1%
 
P1430951.8%
 
K1373291.7%
 
G1293301.6%
 
U1134421.4%
 
F1108131.4%
 
J342640.4%
 
X225890.3%
 
Z94570.1%
 
Other values (3)112580.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII12893092100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
191703314.9%
 
R7972346.2%
 
E7853866.1%
 
A6490945.0%
 
06271394.9%
 
L6197874.8%
 
N5817464.5%
 
D5219844.0%
 
S5106284.0%
 
14980363.9%
 
T4823793.7%
 
O4244143.3%
 
23630192.8%
 
I3455742.7%
 
33174252.5%
 
52768442.1%
 
V2611262.0%
 
42346811.8%
 
W2302691.8%
 
M2195491.7%
 
C2173551.7%
 
H2066141.6%
 
71894231.5%
 
B1864561.4%
 
91841291.4%
 
Other values (22)12457689.7%
 

Zip Code
Real number (ℝ≥0)

SKEWED

Distinct174
Distinct (%)< 0.1%
Missing3515
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean75224.25862
Minimum0
Maximum98004
Zeros1
Zeros (%)< 0.1%
Memory size5.7 MiB
2021-03-12T16:39:10.327013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75203
Q175214
median75224
Q375236
95-th percentile75252
Maximum98004
Range98004
Interquartile range (IQR)22

Descriptive statistics

Standard deviation196.5133045
Coefficient of variation (CV)0.002612366119
Kurtosis85644.66929
Mean75224.25862
Median Absolute Deviation (MAD)11
Skewness-265.7452969
Sum5.621862401e+10
Variance38617.47886
MonotocityNot monotonic
2021-03-12T16:39:10.480715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
75217428995.7%
 
75216389675.2%
 
75228367834.9%
 
75243364114.8%
 
75220344384.6%
 
75211308224.1%
 
75227268353.6%
 
75215266663.6%
 
75204252483.4%
 
75231242593.2%
 
75241226273.0%
 
75206198932.6%
 
75229198322.6%
 
75237196922.6%
 
75201191142.5%
 
75224185382.5%
 
75287172032.3%
 
75208164362.2%
 
75240162032.2%
 
75232151242.0%
 
75212149672.0%
 
75219144001.9%
 
75238142701.9%
 
75235136651.8%
 
75203130271.7%
 
Other values (149)16902822.5%
 
ValueCountFrequency (%) 
01< 0.1%
 
79201< 0.1%
 
123451< 0.1%
 
160661< 0.1%
 
303051< 0.1%
 
330191< 0.1%
 
334361< 0.1%
 
334551< 0.1%
 
482321< 0.1%
 
507041< 0.1%
 
ValueCountFrequency (%) 
980041< 0.1%
 
972241< 0.1%
 
916011< 0.1%
 
908061< 0.1%
 
891481< 0.1%
 
786811< 0.1%
 
775211< 0.1%
 
774491< 0.1%
 
773751< 0.1%
 
773461< 0.1%
 

Type of Incident
Categorical

HIGH CARDINALITY

Distinct1112
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
BMV
82999 
UNAUTHORIZED USE OF MOTOR VEH - AUTOMOBILE
 
36215
FOUND PROPERTY (NO OFFENSE)
 
27075
BURGLARY OF HABITATION - FORCED ENTRY
 
25778
PUBLIC INTOXICATION
 
22524
Other values (1107)
556271 
ValueCountFrequency (%) 
BMV8299911.1%
 
UNAUTHORIZED USE OF MOTOR VEH - AUTOMOBILE362154.8%
 
FOUND PROPERTY (NO OFFENSE)270753.6%
 
BURGLARY OF HABITATION - FORCED ENTRY257783.4%
 
PUBLIC INTOXICATION225243.0%
 
BURGLARY OF BUILDING - FORCED ENTRY216942.9%
 
ABANDONED PROPERTY (NO OFFENSE)201342.7%
 
CRIMINAL TRESPASS WARNING189592.5%
 
CRIM MISCHIEF > OR EQUAL $100 < $750180402.4%
 
ROBBERY OF INDIVIDUAL (AGG)170372.3%
 
CRIM MISCHIEF >OR EQUAL $100 BUT <$750166772.2%
 
UNAUTHORIZED USE OF MOTOR VEH - TRUCK OR BUS147912.0%
 
ASSAULT -OFFENSIVE CONTACT139541.9%
 
RECKLESS DAMAGE136211.8%
 
RECOVERED OUT OF TOWN STOLEN VEHICLE (NO OFFENSE)135301.8%
 
BURGLARY OF HABITATION -NO FORCED ENTRY131931.8%
 
ASSAULT -BODILY INJURY ONLY125431.7%
 
ASSAULT (AGG) -DEADLY WEAPON116781.6%
 
LOST PROPERTY (NO OFFENSE)106071.4%
 
HARASSMENT93911.3%
 
CRIM MISCHIEF > OR EQUAL $50 BUT < $50083381.1%
 
THEFT OF PROP > OR EQUAL $100 <$750 (NOT SHOPLIFT) PC31.03(e2A)83271.1%
 
INJURED PERSON- PUBLIC PROPERTY (OTHER THAN FIREARM) (NO OFFENSE)78541.0%
 
THEFT OF PROP > OR EQUAL $100 BUT <$750- NOT SHOPLIFT71300.9%
 
CRIM MISCHIEF >OR EQUAL $750 < $2,50069110.9%
 
Other values (1087)29186238.9%
 
2021-03-12T16:39:10.665855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique214 ?
Unique (%)< 0.1%
2021-03-12T16:39:10.846434image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length77
Median length34
Mean length32.69935887
Min length3

Overview of Unicode Properties

Unique unicode characters59
Unique unicode categories10 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
337086613.7%
 
O18852137.7%
 
E17355227.1%
 
R14550125.9%
 
T13849155.6%
 
A12073784.9%
 
I12013024.9%
 
N11661894.7%
 
F9494843.9%
 
U9010303.7%
 
S8291993.4%
 
L7108852.9%
 
P6366602.6%
 
C6091942.5%
 
05527792.3%
 
D5486542.2%
 
B5341242.2%
 
M5183102.1%
 
H5153152.1%
 
Y3879241.6%
 
V3175361.3%
 
$3135311.3%
 
(3116721.3%
 
)2985921.2%
 
G2928211.2%
 
Other values (34)19185997.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1824241774.3%
 
Space Separator337086613.7%
 
Decimal Number12093244.9%
 
Math Symbol3346571.4%
 
Currency Symbol3135311.3%
 
Open Punctuation3116721.3%
 
Close Punctuation2985921.2%
 
Dash Punctuation2803861.1%
 
Other Punctuation1537840.6%
 
Lowercase Letter374770.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O188521310.3%
 
E17355229.5%
 
R14550128.0%
 
T13849157.6%
 
A12073786.6%
 
I12013026.6%
 
N11661896.4%
 
F9494845.2%
 
U9010304.9%
 
S8291994.5%
 
L7108853.9%
 
P6366603.5%
 
C6091943.3%
 
D5486543.0%
 
B5341242.9%
 
M5183102.8%
 
H5153152.8%
 
Y3879242.1%
 
V3175361.7%
 
G2928211.6%
 
Q1475150.8%
 
K859540.5%
 
W824250.5%
 
Z695640.4%
 
J376810.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3370866100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-280386100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(311672100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)298592100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
<18071154.0%
 
>15310145.7%
 
+8450.3%
 

Most frequent Currency Symbol characters

ValueCountFrequency (%) 
$313531100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
055277945.7%
 
520144216.7%
 
114299711.8%
 
21027578.5%
 
3984088.1%
 
7892097.4%
 
4178861.5%
 
830480.3%
 
66710.1%
 
9127< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,5547236.1%
 
.5341534.7%
 
/3699124.1%
 
*40472.6%
 
#26771.7%
 
%6560.4%
 
:5260.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e3153484.1%
 
b398210.6%
 
c9572.6%
 
f4991.3%
 
o1490.4%
 
r1490.4%
 
g1410.4%
 
d660.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1827989474.5%
 
Common627281225.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
O188521310.3%
 
E17355229.5%
 
R14550128.0%
 
T13849157.6%
 
A12073786.6%
 
I12013026.6%
 
N11661896.4%
 
F9494845.2%
 
U9010304.9%
 
S8291994.5%
 
L7108853.9%
 
P6366603.5%
 
C6091943.3%
 
D5486543.0%
 
B5341242.9%
 
M5183102.8%
 
H5153152.8%
 
Y3879242.1%
 
V3175361.7%
 
G2928211.6%
 
Q1475150.8%
 
K859540.5%
 
W824250.5%
 
Z695640.4%
 
J376810.2%
 
Other values (9)700880.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
337086653.7%
 
05527798.8%
 
$3135315.0%
 
(3116725.0%
 
)2985924.8%
 
-2803864.5%
 
52014423.2%
 
<1807112.9%
 
>1531012.4%
 
11429972.3%
 
21027571.6%
 
3984081.6%
 
7892091.4%
 
,554720.9%
 
.534150.9%
 
/369910.6%
 
4178860.3%
 
*40470.1%
 
83048< 0.1%
 
#2677< 0.1%
 
+845< 0.1%
 
6671< 0.1%
 
%656< 0.1%
 
:526< 0.1%
 
9127< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII24552706100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
337086613.7%
 
O18852137.7%
 
E17355227.1%
 
R14550125.9%
 
T13849155.6%
 
A12073784.9%
 
I12013024.9%
 
N11661894.7%
 
F9494843.9%
 
U9010303.7%
 
S8291993.4%
 
L7108852.9%
 
P6366602.6%
 
C6091942.5%
 
05527792.3%
 
D5486542.2%
 
B5341242.2%
 
M5183102.1%
 
H5153152.1%
 
Y3879241.6%
 
V3175361.3%
 
$3135311.3%
 
(3116721.3%
 
)2985921.2%
 
G2928211.2%
 
Other values (34)19185997.8%
 

Modus Operandi (MO)
Categorical

HIGH CARDINALITY
MISSING

Distinct426497
Distinct (%)60.9%
Missing50502
Missing (%)6.7%
Memory size5.7 MiB
FOUND PROPERTY
 
13248
CRIMINAL TRESPASS WARNING
 
5675
PUBLIC INTOXICATION
 
4147
ABANDONED PROPERTY
 
3248
INJURED PERSON
 
3245
Other values (426492)
670797 
ValueCountFrequency (%) 
FOUND PROPERTY132481.8%
 
CRIMINAL TRESPASS WARNING56750.8%
 
PUBLIC INTOXICATION41470.6%
 
ABANDONED PROPERTY32480.4%
 
INJURED PERSON32450.4%
 
ABANDONED VEHICLE27580.4%
 
UNEXPLAINED DEATH27290.4%
 
FALSE ALARM23360.3%
 
LOST PROPERTY20400.3%
 
MIR19920.3%
 
CT WARNING19750.3%
 
RECOVERED OUT OF TOWN STOLEN VEHICLE17340.2%
 
APOWW14720.2%
 
UUMV14050.2%
 
BMV14040.2%
 
NATURAL DEATH13290.2%
 
OPEN BUILDING11780.2%
 
AP WAS INTOXICATED IN PUBLIC11340.2%
 
RECOVERED OUT OF TOWN STOLEN10030.1%
 
FOUND PROPERTY.9920.1%
 
CRIMINAL TRESPASS AFFIDAVIT9330.1%
 
CRIMINAL MISCHIEF8060.1%
 
UNK SUSP TOOK COMP'S VEHICLE WITHOUT CONSENT8050.1%
 
UNK SUSP TOOK COMPS VEHICLE WITHOUT CONSENT7970.1%
 
CRIMINAL TRESPASS7830.1%
 
Other values (426472)64119285.4%
 
(Missing)505026.7%
 
2021-03-12T16:39:13.876959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique374071 ?
Unique (%)53.4%
2021-03-12T16:39:14.083099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length399
Median length44
Mean length41.09665558
Min length1

Overview of Unicode Properties

Unique unicode characters68
Unique unicode categories13 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
441061614.3%
 
E25954168.4%
 
O25520928.3%
 
S22144037.2%
 
N20659656.7%
 
T19813026.4%
 
P16721725.4%
 
R13826894.5%
 
I13177534.3%
 
A13155654.3%
 
U12596194.1%
 
C12508574.1%
 
D10504263.4%
 
M7468552.4%
 
K7094522.3%
 
H6712652.2%
 
L6603782.1%
 
W5823211.9%
 
F3686621.2%
 
V3395671.1%
 
G3147081.0%
 
Y2928300.9%
 
B2381320.8%
 
.1852080.6%
 
/1337170.4%
 
Other values (43)5459471.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter2566567083.2%
 
Space Separator441061614.3%
 
Other Punctuation5252901.7%
 
Lowercase Letter1515060.5%
 
Decimal Number598550.2%
 
Open Punctuation176060.1%
 
Close Punctuation175540.1%
 
Dash Punctuation6756< 0.1%
 
Currency Symbol2163< 0.1%
 
Math Symbol803< 0.1%
 
Other Symbol39< 0.1%
 
Connector Punctuation35< 0.1%
 
Modifier Symbol24< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E259541610.1%
 
O25520929.9%
 
S22144038.6%
 
N20659658.0%
 
T19813027.7%
 
P16721726.5%
 
R13826895.4%
 
I13177535.1%
 
A13155655.1%
 
U12596194.9%
 
C12508574.9%
 
D10504264.1%
 
M7468552.9%
 
K7094522.8%
 
H6712652.6%
 
L6603782.6%
 
W5823212.3%
 
F3686621.4%
 
V3395671.3%
 
G3147081.2%
 
Y2928301.1%
 
B2381320.9%
 
X451020.2%
 
J294960.1%
 
Z5743< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
4410616100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.18520835.3%
 
/13371725.5%
 
'12681724.1%
 
,6365612.1%
 
&98181.9%
 
#16720.3%
 
*14100.3%
 
"10980.2%
 
;9640.2%
 
:5780.1%
 
\217< 0.1%
 
@96< 0.1%
 
!26< 0.1%
 
?9< 0.1%
 
%4< 0.1%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(1757999.8%
 
[270.2%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)1754399.9%
 
]110.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n10100466.7%
 
a5050233.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
01322622.1%
 
21301921.8%
 
11081018.1%
 
351338.6%
 
450508.4%
 
536886.2%
 
926094.4%
 
722063.7%
 
621233.5%
 
819913.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-6756100.0%
 

Most frequent Currency Symbol characters

ValueCountFrequency (%) 
$2163100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
<49361.4%
 
>22027.4%
 
=425.2%
 
+405.0%
 
~81.0%
 

Most frequent Other Symbol characters

ValueCountFrequency (%) 
�39100.0%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`24100.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_35100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2581717683.7%
 
Common504074116.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E259541610.1%
 
O25520929.9%
 
S22144038.6%
 
N20659658.0%
 
T19813027.7%
 
P16721726.5%
 
R13826895.4%
 
I13177535.1%
 
A13155655.1%
 
U12596194.9%
 
C12508574.8%
 
D10504264.1%
 
M7468552.9%
 
K7094522.7%
 
H6712652.6%
 
L6603782.6%
 
W5823212.3%
 
F3686621.4%
 
V3395671.3%
 
G3147081.2%
 
Y2928301.1%
 
B2381320.9%
 
n1010040.4%
 
a505020.2%
 
X451020.2%
 
Other values (3)381390.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
441061687.5%
 
.1852083.7%
 
/1337172.7%
 
'1268172.5%
 
,636561.3%
 
(175790.3%
 
)175430.3%
 
0132260.3%
 
2130190.3%
 
1108100.2%
 
&98180.2%
 
-67560.1%
 
351330.1%
 
450500.1%
 
536880.1%
 
926090.1%
 
72206< 0.1%
 
$2163< 0.1%
 
62123< 0.1%
 
81991< 0.1%
 
#1672< 0.1%
 
*1410< 0.1%
 
"1098< 0.1%
 
;964< 0.1%
 
:578< 0.1%
 
Other values (15)1291< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII30857878> 99.9%
 
Specials39< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
441061614.3%
 
E25954168.4%
 
O25520928.3%
 
S22144037.2%
 
N20659656.7%
 
T19813026.4%
 
P16721725.4%
 
R13826894.5%
 
I13177534.3%
 
A13155654.3%
 
U12596194.1%
 
C12508574.1%
 
D10504263.4%
 
M7468552.4%
 
K7094522.3%
 
H6712652.2%
 
L6603782.1%
 
W5823211.9%
 
F3686621.2%
 
V3395671.1%
 
G3147081.0%
 
Y2928300.9%
 
B2381320.8%
 
.1852080.6%
 
/1337170.4%
 
Other values (42)5459081.8%
 

Most frequent Specials characters

ValueCountFrequency (%) 
�39100.0%
 

Victim Condition
Categorical

MISSING

Distinct6
Distinct (%)< 0.1%
Missing727655
Missing (%)96.9%
Memory size5.7 MiB
Good
14204 
Stable
4609 
Deceased
3480 
Serious
 
585
Critical
 
327
ValueCountFrequency (%) 
Good142041.9%
 
Stable46090.6%
 
Deceased34800.5%
 
Serious5850.1%
 
Critical327< 0.1%
 
72< 0.1%
 
(Missing)72765596.9%
 
2021-03-12T16:39:14.299113image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:14.399442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:39:14.614204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length3
Mean length3.065793714
Min length1

Overview of Unicode Properties

Unique unicode characters18
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n145531063.2%
 
a73607132.0%
 
o289931.3%
 
d176840.8%
 
e156340.7%
 
G142040.6%
 
S51940.2%
 
t49360.2%
 
l49360.2%
 
b46090.2%
 
s40650.2%
 
c38070.2%
 
D34800.2%
 
i12390.1%
 
r912< 0.1%
 
u585< 0.1%
 
C327< 0.1%
 
72< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter227878199.0%
 
Uppercase Letter232051.0%
 
Decimal Number2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n145531063.9%
 
a73607132.3%
 
o289931.3%
 
d176840.8%
 
e156340.7%
 
t49360.2%
 
l49360.2%
 
b46090.2%
 
s40650.2%
 
c38070.2%
 
i12390.1%
 
r912< 0.1%
 
u585< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
G1420461.2%
 
S519422.4%
 
D348015.0%
 
C3271.4%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
72100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2301986> 99.9%
 
Common2< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n145531063.2%
 
a73607132.0%
 
o289931.3%
 
d176840.8%
 
e156340.7%
 
G142040.6%
 
S51940.2%
 
t49360.2%
 
l49360.2%
 
b46090.2%
 
s40650.2%
 
c38070.2%
 
D34800.2%
 
i12390.1%
 
r912< 0.1%
 
u585< 0.1%
 
C327< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
72100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2301988100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n145531063.2%
 
a73607132.0%
 
o289931.3%
 
d176840.8%
 
e156340.7%
 
G142040.6%
 
S51940.2%
 
t49360.2%
 
l49360.2%
 
b46090.2%
 
s40650.2%
 
c38070.2%
 
D34800.2%
 
i12390.1%
 
r912< 0.1%
 
u585< 0.1%
 
C327< 0.1%
 
72< 0.1%
 

Victim Injury Description
Categorical

HIGH CARDINALITY
MISSING

Distinct23046
Distinct (%)68.8%
Missing717389
Missing (%)95.5%
Memory size5.7 MiB
DECEASED
 
452
PAIN
 
426
N
 
327
NONE
 
234
BLOODY NOSE
 
163
Other values (23041)
31871 
ValueCountFrequency (%) 
DECEASED4520.1%
 
PAIN4260.1%
 
N327< 0.1%
 
NONE234< 0.1%
 
BLOODY NOSE163< 0.1%
 
BUSTED LIP162< 0.1%
 
DEATH132< 0.1%
 
GUNSHOT WOUND126< 0.1%
 
HEAD INJURY101< 0.1%
 
DOG BITE96< 0.1%
 
GUN SHOT WOUND86< 0.1%
 
UNKNOWN82< 0.1%
 
SWOLLEN LEFT EYE76< 0.1%
 
SCRATCHES73< 0.1%
 
BLOODY LIP64< 0.1%
 
GSW60< 0.1%
 
SWOLLEN RIGHT EYE59< 0.1%
 
BLUNT FORCE TRAUMA59< 0.1%
 
KILLED59< 0.1%
 
SWOLLEN LIP54< 0.1%
 
OVERDOSE54< 0.1%
 
NECK PAIN45< 0.1%
 
BACK PAIN44< 0.1%
 
CUT ON HEAD44< 0.1%
 
LACERATION TO HEAD42< 0.1%
 
Other values (23021)303534.0%
 
(Missing)71738995.5%
 
2021-03-12T16:39:14.880118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique20529 ?
Unique (%)61.3%
2021-03-12T16:39:15.039419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length98
Median length3
Mean length3.969832006
Min length1

Overview of Unicode Properties

Unique unicode characters80
Unique unicode categories10 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n143478248.1%
 
a71739524.1%
 
1203274.0%
 
E804842.7%
 
N567611.9%
 
O552091.9%
 
T525591.8%
 
A522531.8%
 
S454351.5%
 
I426621.4%
 
R418091.4%
 
L370821.2%
 
D314951.1%
 
C301421.0%
 
H301351.0%
 
U230850.8%
 
F205690.7%
 
B192340.6%
 
G178130.6%
 
P147030.5%
 
M116270.4%
 
W104540.4%
 
K103570.3%
 
,66440.2%
 
Y65010.2%
 
Other values (55)112790.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter215226372.2%
 
Uppercase Letter69507423.3%
 
Space Separator1203274.0%
 
Other Punctuation111420.4%
 
Decimal Number1161< 0.1%
 
Dash Punctuation317< 0.1%
 
Close Punctuation252< 0.1%
 
Open Punctuation248< 0.1%
 
Math Symbol7< 0.1%
 
Modifier Symbol5< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n143478266.7%
 
a71739533.3%
 
s13< 0.1%
 
e13< 0.1%
 
t11< 0.1%
 
h7< 0.1%
 
o6< 0.1%
 
u5< 0.1%
 
p5< 0.1%
 
b4< 0.1%
 
r4< 0.1%
 
d3< 0.1%
 
c3< 0.1%
 
i3< 0.1%
 
m2< 0.1%
 
g2< 0.1%
 
k1< 0.1%
 
y1< 0.1%
 
w1< 0.1%
 
v1< 0.1%
 
l1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E8048411.6%
 
N567618.2%
 
O552097.9%
 
T525597.6%
 
A522537.5%
 
S454356.5%
 
I426626.1%
 
R418096.0%
 
L370825.3%
 
D314954.5%
 
C301424.3%
 
H301354.3%
 
U230853.3%
 
F205693.0%
 
B192342.8%
 
G178132.6%
 
P147032.1%
 
M116271.7%
 
W104541.5%
 
K103571.5%
 
Y65010.9%
 
V20900.3%
 
J18870.3%
 
X4110.1%
 
Z218< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
120327100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,664459.6%
 
/234721.1%
 
.129011.6%
 
&3192.9%
 
'1851.7%
 
;1771.6%
 
"1151.0%
 
:360.3%
 
#110.1%
 
?80.1%
 
*4< 0.1%
 
\3< 0.1%
 
@2< 0.1%
 
%1< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-317100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
238433.1%
 
129825.7%
 
315213.1%
 
411610.0%
 
5796.8%
 
0373.2%
 
6332.8%
 
7312.7%
 
8201.7%
 
9110.9%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(24699.2%
 
[20.8%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)25099.2%
 
]20.8%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+571.4%
 
~228.6%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`5100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin284733795.5%
 
Common1334594.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n143478250.4%
 
a71739525.2%
 
E804842.8%
 
N567612.0%
 
O552091.9%
 
T525591.8%
 
A522531.8%
 
S454351.6%
 
I426621.5%
 
R418091.5%
 
L370821.3%
 
D314951.1%
 
C301421.1%
 
H301351.1%
 
U230850.8%
 
F205690.7%
 
B192340.7%
 
G178130.6%
 
P147030.5%
 
M116270.4%
 
W104540.4%
 
K103570.4%
 
Y65010.2%
 
V20900.1%
 
J18870.1%
 
Other values (22)814< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
12032790.2%
 
,66445.0%
 
/23471.8%
 
.12901.0%
 
23840.3%
 
&3190.2%
 
-3170.2%
 
12980.2%
 
)2500.2%
 
(2460.2%
 
'1850.1%
 
;1770.1%
 
31520.1%
 
41160.1%
 
"1150.1%
 
5790.1%
 
037< 0.1%
 
:36< 0.1%
 
633< 0.1%
 
731< 0.1%
 
820< 0.1%
 
#11< 0.1%
 
911< 0.1%
 
?8< 0.1%
 
+5< 0.1%
 
Other values (8)21< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2980796100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n143478248.1%
 
a71739524.1%
 
1203274.0%
 
E804842.7%
 
N567611.9%
 
O552091.9%
 
T525591.8%
 
A522531.8%
 
S454351.5%
 
I426621.4%
 
R418091.4%
 
L370821.2%
 
D314951.1%
 
C301421.0%
 
H301351.0%
 
U230850.8%
 
F205690.7%
 
B192340.6%
 
G178130.6%
 
P147030.5%
 
M116270.4%
 
W104540.4%
 
K103570.3%
 
,66440.2%
 
Y65010.2%
 
Other values (55)112790.4%
 

Victim Gender
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing280269
Missing (%)37.3%
Memory size5.7 MiB
Male
257203 
Female
211564 
Unknown
 
1816
TEST
 
9
N
 
1
ValueCountFrequency (%) 
Male25720334.3%
 
Female21156428.2%
 
Unknown18160.2%
 
TEST9< 0.1%
 
N1< 0.1%
 
(Missing)28026937.3%
 
2021-03-12T16:39:15.211928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-03-12T16:39:15.303051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:39:15.463622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length4
Mean length4.19751166
Min length1

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a74903623.8%
 
e68033121.6%
 
n56598618.0%
 
l46876714.9%
 
M2572038.2%
 
F2115646.7%
 
m2115646.7%
 
U18160.1%
 
k18160.1%
 
o18160.1%
 
w18160.1%
 
T18< 0.1%
 
E9< 0.1%
 
S9< 0.1%
 
N1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter268113285.1%
 
Uppercase Letter47062014.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a74903627.9%
 
e68033125.4%
 
n56598621.1%
 
l46876717.5%
 
m2115647.9%
 
k18160.1%
 
o18160.1%
 
w18160.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M25720354.7%
 
F21156445.0%
 
U18160.4%
 
T18< 0.1%
 
E9< 0.1%
 
S9< 0.1%
 
N1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3151752100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a74903623.8%
 
e68033121.6%
 
n56598618.0%
 
l46876714.9%
 
M2572038.2%
 
F2115646.7%
 
m2115646.7%
 
U18160.1%
 
k18160.1%
 
o18160.1%
 
w18160.1%
 
T18< 0.1%
 
E9< 0.1%
 
S9< 0.1%
 
N1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3151752100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a74903623.8%
 
e68033121.6%
 
n56598618.0%
 
l46876714.9%
 
M2572038.2%
 
F2115646.7%
 
m2115646.7%
 
U18160.1%
 
k18160.1%
 
o18160.1%
 
w18160.1%
 
T18< 0.1%
 
E9< 0.1%
 
S9< 0.1%
 
N1< 0.1%
 

Person Involvement Type
Categorical

MISSING

Distinct8
Distinct (%)< 0.1%
Missing27637
Missing (%)3.7%
Memory size5.7 MiB
Victim
690002 
Registered Owner
 
28969
Owner
 
3014
Stln Vehicle (UUMV)
 
799
Driver
 
355
Other values (3)
 
86
ValueCountFrequency (%) 
Victim69000291.9%
 
Registered Owner289693.9%
 
Owner30140.4%
 
Stln Vehicle (UUMV)7990.1%
 
Driver355< 0.1%
 
Passenger45< 0.1%
 
Reporting Person24< 0.1%
 
Witness17< 0.1%
 
(Missing)276373.7%
 
2021-03-12T16:39:15.607513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:15.716841image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:39:15.941887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length6
Mean length6.285730267
Min length3

Overview of Unicode Properties

Unique unicode characters29
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i141016829.9%
 
t71981115.3%
 
V69160014.7%
 
c69080114.6%
 
m69000214.6%
 
e1209982.6%
 
n881661.9%
 
r617551.3%
 
O319830.7%
 
w319830.7%
 
305910.6%
 
s291170.6%
 
g290380.6%
 
R289930.6%
 
d289690.6%
 
a276820.6%
 
l1598< 0.1%
 
U1598< 0.1%
 
S799< 0.1%
 
h799< 0.1%
 
(799< 0.1%
 
M799< 0.1%
 
)799< 0.1%
 
D355< 0.1%
 
v355< 0.1%
 
Other values (4)158< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter393131483.3%
 
Uppercase Letter75621316.0%
 
Space Separator305910.6%
 
Open Punctuation799< 0.1%
 
Close Punctuation799< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i141016835.9%
 
t71981118.3%
 
c69080117.6%
 
m69000217.6%
 
e1209983.1%
 
n881662.2%
 
r617551.6%
 
w319830.8%
 
s291170.7%
 
g290380.7%
 
d289690.7%
 
a276820.7%
 
l1598< 0.1%
 
h799< 0.1%
 
v355< 0.1%
 
o48< 0.1%
 
p24< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
V69160091.5%
 
O319834.2%
 
R289933.8%
 
U15980.2%
 
S7990.1%
 
M7990.1%
 
D355< 0.1%
 
P69< 0.1%
 
W17< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
30591100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(799100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)799100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin468752799.3%
 
Common321890.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i141016830.1%
 
t71981115.4%
 
V69160014.8%
 
c69080114.7%
 
m69000214.7%
 
e1209982.6%
 
n881661.9%
 
r617551.3%
 
O319830.7%
 
w319830.7%
 
s291170.6%
 
g290380.6%
 
R289930.6%
 
d289690.6%
 
a276820.6%
 
l1598< 0.1%
 
U1598< 0.1%
 
S799< 0.1%
 
h799< 0.1%
 
M799< 0.1%
 
D355< 0.1%
 
v355< 0.1%
 
P69< 0.1%
 
o48< 0.1%
 
p24< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
3059195.0%
 
(7992.5%
 
)7992.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4719716100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i141016829.9%
 
t71981115.3%
 
V69160014.7%
 
c69080114.6%
 
m69000214.6%
 
e1209982.6%
 
n881661.9%
 
r617551.3%
 
O319830.7%
 
w319830.7%
 
305910.6%
 
s291170.6%
 
g290380.6%
 
R289930.6%
 
d289690.6%
 
a276820.6%
 
l1598< 0.1%
 
U1598< 0.1%
 
S799< 0.1%
 
h799< 0.1%
 
(799< 0.1%
 
M799< 0.1%
 
)799< 0.1%
 
D355< 0.1%
 
v355< 0.1%
 
Other values (4)158< 0.1%
 

Incident Number w/year
Categorical

HIGH CARDINALITY
UNIFORM

Distinct700639
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
216100-2017
 
139
107406-2019
 
24
073944-2016
 
23
211119-2019
 
22
053209-2020
 
21
Other values (700634)
750633 
ValueCountFrequency (%) 
216100-2017139< 0.1%
 
107406-201924< 0.1%
 
073944-201623< 0.1%
 
211119-201922< 0.1%
 
053209-202021< 0.1%
 
186195-201918< 0.1%
 
246967-201818< 0.1%
 
080577-201917< 0.1%
 
013407-202117< 0.1%
 
054584-201716< 0.1%
 
171080-201816< 0.1%
 
036579-202116< 0.1%
 
018178-201916< 0.1%
 
019483-202016< 0.1%
 
017790-202116< 0.1%
 
198261-202015< 0.1%
 
102301-201714< 0.1%
 
202943-201814< 0.1%
 
015274-201914< 0.1%
 
124850-201514< 0.1%
 
278058-201414< 0.1%
 
018187-201913< 0.1%
 
097483-201813< 0.1%
 
067424-201712< 0.1%
 
036794-202112< 0.1%
 
Other values (700614)75033299.9%
 
2021-03-12T16:39:20.443397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique663364 ?
Unique (%)88.3%
2021-03-12T16:39:20.625624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length11
Mean length11.00025837
Min length6

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0153701018.6%
 
2149871118.1%
 
1126578515.3%
 
-7507559.1%
 
85067376.1%
 
94899795.9%
 
64701035.7%
 
54665535.6%
 
74598655.6%
 
44286715.2%
 
33854914.7%
 
B9< 0.1%
 
6< 0.1%
 
/1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number750890590.9%
 
Dash Punctuation7507559.1%
 
Uppercase Letter9< 0.1%
 
Space Separator6< 0.1%
 
Other Punctuation1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0153701020.5%
 
2149871120.0%
 
1126578516.9%
 
85067376.7%
 
94899796.5%
 
64701036.3%
 
54665536.2%
 
74598656.1%
 
44286715.7%
 
33854915.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-750755100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B9100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
6100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common8259667> 99.9%
 
Latin9< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0153701018.6%
 
2149871118.1%
 
1126578515.3%
 
-7507559.1%
 
85067376.1%
 
94899795.9%
 
64701035.7%
 
54665535.6%
 
74598655.6%
 
44286715.2%
 
33854914.7%
 
6< 0.1%
 
/1< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
B9100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII8259676100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0153701018.6%
 
2149871118.1%
 
1126578515.3%
 
-7507559.1%
 
85067376.1%
 
94899795.9%
 
64701035.7%
 
54665535.6%
 
74598655.6%
 
44286715.2%
 
33854914.7%
 
B9< 0.1%
 
6< 0.1%
 
/1< 0.1%
 

Year of Incident
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.551464
Minimum2005
Maximum2121
Zeros0
Zeros (%)0.0%
Memory size5.7 MiB
2021-03-12T16:39:20.747257image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2014
Q12016
median2018
Q32019
95-th percentile2020
Maximum2121
Range116
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.986633577
Coefficient of variation (CV)0.0009846755401
Kurtosis8.679808785
Mean2017.551464
Median Absolute Deviation (MAD)2
Skewness-0.01621861675
Sum1514902727
Variance3.946712969
MonotocityNot monotonic
2021-03-12T16:39:20.879159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
202013430717.9%
 
201912982617.3%
 
201812010516.0%
 
20169994013.3%
 
20179497612.6%
 
20159320312.4%
 
2014566037.5%
 
2021218832.9%
 
201315< 0.1%
 
21211< 0.1%
 
20051< 0.1%
 
20091< 0.1%
 
20111< 0.1%
 
ValueCountFrequency (%) 
20051< 0.1%
 
20091< 0.1%
 
20111< 0.1%
 
201315< 0.1%
 
2014566037.5%
 
20159320312.4%
 
20169994013.3%
 
20179497612.6%
 
201812010516.0%
 
201912982617.3%
 
ValueCountFrequency (%) 
21211< 0.1%
 
2021218832.9%
 
202013430717.9%
 
201912982617.3%
 
201812010516.0%
 
20179497612.6%
 
20169994013.3%
 
20159320312.4%
 
2014566037.5%
 
201315< 0.1%
 

Service Number ID
Categorical

UNIQUE

Distinct750862
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
173201-2020-01
 
1
184563-2015-01
 
1
812341-2020-01
 
1
163826-2016-01
 
1
266040-2014-01
 
1
Other values (750857)
750857 
ValueCountFrequency (%) 
173201-2020-011< 0.1%
 
184563-2015-011< 0.1%
 
812341-2020-011< 0.1%
 
163826-2016-011< 0.1%
 
266040-2014-011< 0.1%
 
095178-2019-011< 0.1%
 
068894-2019-011< 0.1%
 
131396-2019-011< 0.1%
 
200196-2016-011< 0.1%
 
259407-2018-011< 0.1%
 
048625-2017-011< 0.1%
 
035387-2018-011< 0.1%
 
044495-2017-011< 0.1%
 
802857-2019-011< 0.1%
 
030862-2016-011< 0.1%
 
130043-2019-011< 0.1%
 
209623-2019-011< 0.1%
 
119631-2017-011< 0.1%
 
050715-2020-011< 0.1%
 
101625-2020-011< 0.1%
 
237134-2017-011< 0.1%
 
139369-2020-021< 0.1%
 
141804-2020-011< 0.1%
 
046676-2020-051< 0.1%
 
120113-2015-011< 0.1%
 
Other values (750837)750837> 99.9%
 
2021-03-12T16:39:26.130704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique750862 ?
Unique (%)100.0%
2021-03-12T16:39:26.309927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length14
Mean length14.00050742
Min length13

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0228762221.8%
 
1196674118.7%
 
2153607914.6%
 
-150172414.3%
 
85069034.8%
 
94900824.7%
 
64705974.5%
 
54675364.4%
 
74601114.4%
 
44314274.1%
 
33936213.7%
 
5< 0.1%
 
B1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number901071985.7%
 
Dash Punctuation150172414.3%
 
Space Separator5< 0.1%
 
Uppercase Letter1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0228762225.4%
 
1196674121.8%
 
2153607917.0%
 
85069035.6%
 
94900825.4%
 
64705975.2%
 
54675365.2%
 
74601115.1%
 
44314274.8%
 
33936214.4%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1501724100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B1100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
5100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common10512448> 99.9%
 
Latin1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0228762221.8%
 
1196674118.7%
 
2153607914.6%
 
-150172414.3%
 
85069034.8%
 
94900824.7%
 
64705974.5%
 
54675364.4%
 
74601114.4%
 
44314274.1%
 
33936213.7%
 
5< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
B1100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII10512449100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0228762221.8%
 
1196674118.7%
 
2153607914.6%
 
-150172414.3%
 
85069034.8%
 
94900824.7%
 
64705974.5%
 
54675364.4%
 
74601114.4%
 
44314274.1%
 
33936213.7%
 
5< 0.1%
 
B1< 0.1%
 

Watch
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
1
290234 
3
240695 
2
219933 
ValueCountFrequency (%) 
129023438.7%
 
324069532.1%
 
221993329.3%
 
2021-03-12T16:39:26.484246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:26.621248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:39:26.761337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
129023438.7%
 
324069532.1%
 
221993329.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number750862100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
129023438.7%
 
324069532.1%
 
221993329.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common750862100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
129023438.7%
 
324069532.1%
 
221993329.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII750862100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
129023438.7%
 
324069532.1%
 
221993329.3%
 

Call (911) Problem
Categorical

HIGH CARDINALITY
MISSING

Distinct117
Distinct (%)< 0.1%
Missing28075
Missing (%)3.7%
Memory size5.7 MiB
58 - ROUTINE INVESTIGATION
85454 
11V - BURG MOTOR VEH
67314 
6X - MAJOR DIST (VIOLENCE)
59534 
09V - UUMV
42814 
11R - BURG OF RES
 
40600
Other values (112)
427071 
ValueCountFrequency (%) 
58 - ROUTINE INVESTIGATION8545411.4%
 
11V - BURG MOTOR VEH673149.0%
 
6X - MAJOR DIST (VIOLENCE)595347.9%
 
09V - UUMV428145.7%
 
11R - BURG OF RES406005.4%
 
40/01 - OTHER330254.4%
 
09 - THEFT303404.0%
 
31 - CRIMINAL MISCHIEF280403.7%
 
40 - OTHER271933.6%
 
20 - ROBBERY229823.1%
 
07 - MINOR ACCIDENT223793.0%
 
PSE/09 - THEFT198022.6%
 
11B - BURG OF BUS190892.5%
 
24 - ABANDONED PROPERTY186272.5%
 
7X - MAJOR ACCIDENT141501.9%
 
55 - TRAFFIC STOP133271.8%
 
ODJ - OFF DUTY JOB93601.2%
 
09/01 - THEFT88051.2%
 
PSE/31- CRIMINAL MISCHIEF84561.1%
 
12B - BUSINESS ALARM75271.0%
 
PSE/11V - BURG MOTOR VEH72061.0%
 
6XA - MAJOR DIST AMBULANCE71050.9%
 
**PD REQUESTED BY FIRE68720.9%
 
11V/01 - BURG MOTOR VEH66680.9%
 
16 - INJURED PERSON60040.8%
 
Other values (92)11011414.7%
 
(Missing)280753.7%
 
2021-03-12T16:39:26.949152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2021-03-12T16:39:27.111915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length20
Mean length18.81775213
Min length3

Overview of Unicode Properties

Unique unicode characters48
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
224110315.9%
 
E9419696.7%
 
R9119306.5%
 
O8630976.1%
 
I8274565.9%
 
T7855495.6%
 
-7242515.1%
 
N5802504.1%
 
S4947603.5%
 
14753263.4%
 
U4419653.1%
 
A4309983.1%
 
V4271113.0%
 
M3617442.6%
 
B3453582.4%
 
C2999892.1%
 
02934802.1%
 
G2904022.1%
 
H2629621.9%
 
F2530711.8%
 
D2396391.7%
 
P1542491.1%
 
L1398651.0%
 
/1234010.9%
 
51177350.8%
 
Other values (23)11018757.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter935916566.2%
 
Space Separator224110315.9%
 
Decimal Number145507210.3%
 
Dash Punctuation7242515.1%
 
Other Punctuation1409391.0%
 
Lowercase Letter842250.6%
 
Open Punctuation614550.4%
 
Close Punctuation614550.4%
 
Math Symbol1870< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
147532632.7%
 
029348020.2%
 
51177358.1%
 
91165168.0%
 
41012247.0%
 
8885546.1%
 
6829275.7%
 
2745025.1%
 
3591824.1%
 
7456263.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2241103100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-724251100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E94196910.1%
 
R9119309.7%
 
O8630979.2%
 
I8274568.8%
 
T7855498.4%
 
N5802506.2%
 
S4947605.3%
 
U4419654.7%
 
A4309984.6%
 
V4271114.6%
 
M3617443.9%
 
B3453583.7%
 
C2999893.2%
 
G2904023.1%
 
H2629622.8%
 
F2530712.7%
 
D2396392.6%
 
P1542491.6%
 
L1398651.5%
 
J1139521.2%
 
X912101.0%
 
Y738380.8%
 
K103290.1%
 
W101640.1%
 
Q73080.1%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(61455100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)61455100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/12340187.6%
 
*137449.8%
 
,25281.8%
 
#12570.9%
 
.5< 0.1%
 
'4< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n5615066.7%
 
a2807533.3%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+1870100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin944339066.8%
 
Common468614533.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
224110347.8%
 
-72425115.5%
 
147532610.1%
 
02934806.3%
 
/1234012.6%
 
51177352.5%
 
91165162.5%
 
41012242.2%
 
8885541.9%
 
6829271.8%
 
2745021.6%
 
(614551.3%
 
)614551.3%
 
3591821.3%
 
7456261.0%
 
*137440.3%
 
,25280.1%
 
+1870< 0.1%
 
#1257< 0.1%
 
.5< 0.1%
 
'4< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E94196910.0%
 
R9119309.7%
 
O8630979.1%
 
I8274568.8%
 
T7855498.3%
 
N5802506.1%
 
S4947605.2%
 
U4419654.7%
 
A4309984.6%
 
V4271114.5%
 
M3617443.8%
 
B3453583.7%
 
C2999893.2%
 
G2904023.1%
 
H2629622.8%
 
F2530712.7%
 
D2396392.5%
 
P1542491.6%
 
L1398651.5%
 
J1139521.2%
 
X912101.0%
 
Y738380.8%
 
n561500.6%
 
a280750.3%
 
K103290.1%
 
Other values (2)174720.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII14129535100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
224110315.9%
 
E9419696.7%
 
R9119306.5%
 
O8630976.1%
 
I8274565.9%
 
T7855495.6%
 
-7242515.1%
 
N5802504.1%
 
S4947603.5%
 
14753263.4%
 
U4419653.1%
 
A4309983.1%
 
V4271113.0%
 
M3617442.6%
 
B3453582.4%
 
C2999892.1%
 
02934802.1%
 
G2904022.1%
 
H2629621.9%
 
F2530711.8%
 
D2396391.7%
 
P1542491.1%
 
L1398651.0%
 
/1234010.9%
 
51177350.8%
 
Other values (23)11018757.8%
 

Type Location
Categorical

HIGH CARDINALITY

Distinct73
Distinct (%)< 0.1%
Missing1199
Missing (%)0.2%
Memory size5.7 MiB
Highway, Street, Alley ETC
133142 
Single Family Residence - Occupied
89865 
Apartment Parking Lot
65206 
Apartment Complex/Building
51910 
Parking Lot (All Others)
49282 
Other values (68)
360258 
ValueCountFrequency (%) 
Highway, Street, Alley ETC13314217.7%
 
Single Family Residence - Occupied8986512.0%
 
Apartment Parking Lot652068.7%
 
Apartment Complex/Building519106.9%
 
Parking Lot (All Others)492826.6%
 
Outdoor Area Public/Private471056.3%
 
Apartment Residence442785.9%
 
Parking (Business)339264.5%
 
Other315074.2%
 
Retail Store234513.1%
 
Convenience Store183242.4%
 
Restaurant/Food Service/TABC Location179842.4%
 
Business Office154322.1%
 
Commercial Property Occupied/Vacant152702.0%
 
Gas or Service Station131831.8%
 
Single Family Residence - Vacant101371.4%
 
Parking Lot (Apartment)84591.1%
 
Motor Vehicle79871.1%
 
Hotel/Motel/ETC78731.0%
 
Bar/NightClub/DanceHall ETC.69870.9%
 
Storage Facility50460.7%
 
Government Facility38850.5%
 
Grocery/Supermarket31930.4%
 
Park31870.4%
 
Medical Facility29180.4%
 
Other values (48)401265.3%
 
2021-03-12T16:39:27.304059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:27.486916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length26
Mean length23.6323066
Min length3

Overview of Unicode Properties

Unique unicode characters52
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e186176610.5%
 
15580868.8%
 
i12377257.0%
 
t11971666.7%
 
r9194635.2%
 
a9189925.2%
 
n8986465.1%
 
l8438944.8%
 
c5815973.3%
 
o5715633.2%
 
g4727662.7%
 
A4264052.4%
 
y4053712.3%
 
s4029222.3%
 
d3906432.2%
 
m3879752.2%
 
u3628892.0%
 
p3588752.0%
 
S3444171.9%
 
P2830661.6%
 
C2731601.5%
 
,2662841.5%
 
O2496381.4%
 
h2436981.4%
 
/2182861.2%
 
Other values (27)206930811.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1262473971.1%
 
Uppercase Letter277604215.6%
 
Space Separator15580868.8%
 
Other Punctuation4915572.8%
 
Dash Punctuation1043410.6%
 
Open Punctuation949180.5%
 
Close Punctuation949180.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A42640515.4%
 
S34441712.4%
 
P28306610.2%
 
C2731609.8%
 
O2496389.0%
 
R1928266.9%
 
T1776386.4%
 
H1502215.4%
 
E1499505.4%
 
L1478295.3%
 
F1360294.9%
 
B1304994.7%
 
V346601.2%
 
M252090.9%
 
G233960.8%
 
D140590.5%
 
N82800.3%
 
I60760.2%
 
U16560.1%
 
W843< 0.1%
 
J185< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e186176614.7%
 
i12377259.8%
 
t11971669.5%
 
r9194637.3%
 
a9189927.3%
 
n8986467.1%
 
l8438946.7%
 
c5815974.6%
 
o5715634.5%
 
g4727663.7%
 
y4053713.2%
 
s4029223.2%
 
d3906433.1%
 
m3879753.1%
 
u3628892.9%
 
p3588752.8%
 
h2436981.9%
 
k1760411.4%
 
w1402861.1%
 
v1059840.8%
 
b563800.4%
 
x519100.4%
 
f363700.3%
 
q1817< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1558086100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,26628454.2%
 
/21828644.4%
 
.69871.4%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(94918100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)94918100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-104341100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1540078186.8%
 
Common234382013.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e186176612.1%
 
i12377258.0%
 
t11971667.8%
 
r9194636.0%
 
a9189926.0%
 
n8986465.8%
 
l8438945.5%
 
c5815973.8%
 
o5715633.7%
 
g4727663.1%
 
A4264052.8%
 
y4053712.6%
 
s4029222.6%
 
d3906432.5%
 
m3879752.5%
 
u3628892.4%
 
p3588752.3%
 
S3444172.2%
 
P2830661.8%
 
C2731601.8%
 
O2496381.6%
 
h2436981.6%
 
R1928261.3%
 
T1776381.2%
 
k1760411.1%
 
Other values (20)12216397.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
155808666.5%
 
,26628411.4%
 
/2182869.3%
 
-1043414.5%
 
(949184.0%
 
)949184.0%
 
.69870.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII17744601100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e186176610.5%
 
15580868.8%
 
i12377257.0%
 
t11971666.7%
 
r9194635.2%
 
a9189925.2%
 
n8986465.1%
 
l8438944.8%
 
c5815973.3%
 
o5715633.2%
 
g4727662.7%
 
A4264052.4%
 
y4053712.3%
 
s4029222.3%
 
d3906432.2%
 
m3879752.2%
 
u3628892.0%
 
p3588752.0%
 
S3444171.9%
 
P2830661.6%
 
C2731601.5%
 
,2662841.5%
 
O2496381.4%
 
h2436981.4%
 
/2182861.2%
 
Other values (27)206930811.7%
 

Type of Property
Categorical

MISSING

Distinct25
Distinct (%)< 0.1%
Missing580428
Missing (%)77.3%
Memory size5.7 MiB
Motor Vehicle
34987 
Other
24612 
Apartment Complex/Building
21044 
Residential Property Occupied/Vacant
20690 
Parking Lot
17392 
Other values (20)
51709 
ValueCountFrequency (%) 
Motor Vehicle349874.7%
 
Other246123.3%
 
Apartment Complex/Building210442.8%
 
Residential Property Occupied/Vacant206902.8%
 
Parking Lot173922.3%
 
None121071.6%
 
Outdoor Area Public/Private114011.5%
 
Commercial Property Occupied/Vacant85671.1%
 
Retail Store77381.0%
 
Resturant/Food Service/Tabc Location48200.6%
 
Business Office35390.5%
 
Religious Institution7510.1%
 
Medical Facility6330.1%
 
Financial Institution5670.1%
 
Goverment Facility4720.1%
 
Entertainment/Sports Venue4550.1%
 
ATM3770.1%
 
Personal Services260< 0.1%
 
Foster Home14< 0.1%
 
9372< 0.1%
 
9102< 0.1%
 
9201< 0.1%
 
5101< 0.1%
 
9131< 0.1%
 
9321< 0.1%
 
(Missing)58042877.3%
 
2021-03-12T16:39:27.680151image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2021-03-12T16:39:27.862592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length3
Mean length6.448445919
Min length3

Overview of Unicode Properties

Unique unicode characters48
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n130189126.9%
 
a75522215.6%
 
e3220146.7%
 
t2583865.3%
 
i2297594.7%
 
r2186134.5%
 
o2114454.4%
 
1788083.7%
 
c1632903.4%
 
l1287872.7%
 
p1010572.1%
 
d878451.8%
 
u839861.7%
 
/717971.5%
 
P697111.4%
 
O688091.4%
 
V646991.3%
 
m601631.2%
 
h595991.2%
 
g391870.8%
 
s391850.8%
 
M359970.7%
 
R339990.7%
 
A328220.7%
 
y303620.6%
 
Other values (23)1944604.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter416947986.1%
 
Uppercase Letter4217858.7%
 
Space Separator1788083.7%
 
Other Punctuation717971.5%
 
Decimal Number24< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n130189131.2%
 
a75522218.1%
 
e3220147.7%
 
t2583866.2%
 
i2297595.5%
 
r2186135.2%
 
o2114455.1%
 
c1632903.9%
 
l1287873.1%
 
p1010572.4%
 
d878452.1%
 
u839862.0%
 
m601631.4%
 
h595991.4%
 
g391870.9%
 
s391850.9%
 
y303620.7%
 
x210440.5%
 
k173920.4%
 
v169530.4%
 
b162210.4%
 
f70780.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
P6971116.5%
 
O6880916.3%
 
V6469915.3%
 
M359978.5%
 
R339998.1%
 
A328227.8%
 
C296117.0%
 
B245835.8%
 
L222125.3%
 
S132733.1%
 
N121072.9%
 
F65061.5%
 
T51971.2%
 
I13180.3%
 
G4720.1%
 
E4550.1%
 
H14< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
178808100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/71797100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
9729.2%
 
1416.7%
 
0416.7%
 
3416.7%
 
228.3%
 
728.3%
 
514.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin459126494.8%
 
Common2506295.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n130189128.4%
 
a75522216.4%
 
e3220147.0%
 
t2583865.6%
 
i2297595.0%
 
r2186134.8%
 
o2114454.6%
 
c1632903.6%
 
l1287872.8%
 
p1010572.2%
 
d878451.9%
 
u839861.8%
 
P697111.5%
 
O688091.5%
 
V646991.4%
 
m601631.3%
 
h595991.3%
 
g391870.9%
 
s391850.9%
 
M359970.8%
 
R339990.7%
 
A328220.7%
 
y303620.7%
 
C296110.6%
 
B245830.5%
 
Other values (14)1402423.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
17880871.3%
 
/7179728.6%
 
97< 0.1%
 
14< 0.1%
 
04< 0.1%
 
34< 0.1%
 
22< 0.1%
 
72< 0.1%
 
51< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4841893100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n130189126.9%
 
a75522215.6%
 
e3220146.7%
 
t2583865.3%
 
i2297594.7%
 
r2186134.5%
 
o2114454.4%
 
1788083.7%
 
c1632903.4%
 
l1287872.7%
 
p1010572.1%
 
d878451.8%
 
u839861.7%
 
/717971.5%
 
P697111.4%
 
O688091.4%
 
V646991.3%
 
m601631.2%
 
h595991.2%
 
g391870.8%
 
s391850.8%
 
M359970.7%
 
R339990.7%
 
A328220.7%
 
y303620.6%
 
Other values (23)1944604.0%
 

Victim Type
Categorical

MISSING

Distinct9
Distinct (%)< 0.1%
Missing35768
Missing (%)4.8%
Memory size5.7 MiB
Individual
465759 
Business
112700 
Government
88896 
Society/Public
 
45631
Religious Organizati
 
949
Other values (4)
 
1159
ValueCountFrequency (%) 
Individual46575962.0%
 
Business11270015.0%
 
Government8889611.8%
 
Society/Public456316.1%
 
Religious Organizati9490.1%
 
Financial Institutio7820.1%
 
Law Enforcement Offi355< 0.1%
 
Other18< 0.1%
 
Unknown4< 0.1%
 
(Missing)357684.8%
 
2021-03-12T16:39:28.031392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:28.683937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:39:29.009573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length10
Mean length9.637091769
Min length3

Overview of Unicode Properties

Unique unicode characters35
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i114275915.8%
 
d93151812.9%
 
n83180411.5%
 
u6258218.6%
 
v5546557.7%
 
l5131217.1%
 
a5053447.0%
 
I4665416.4%
 
s3398314.7%
 
e3378004.7%
 
t1381951.9%
 
o1366171.9%
 
B1127001.6%
 
c923991.3%
 
r902181.2%
 
m892511.2%
 
G888961.2%
 
S456310.6%
 
y456310.6%
 
/456310.6%
 
P456310.6%
 
b456310.6%
 
2441< 0.1%
 
g1898< 0.1%
 
O1322< 0.1%
 
Other values (10)48400.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter642488888.8%
 
Uppercase Letter76316610.5%
 
Other Punctuation456310.6%
 
Space Separator2441< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i114275917.8%
 
d93151814.5%
 
n83180412.9%
 
u6258219.7%
 
v5546558.6%
 
l5131218.0%
 
a5053447.9%
 
s3398315.3%
 
e3378005.3%
 
t1381952.2%
 
o1366172.1%
 
c923991.4%
 
r902181.4%
 
m892511.4%
 
y456310.7%
 
b456310.7%
 
g1898< 0.1%
 
f1065< 0.1%
 
z949< 0.1%
 
w359< 0.1%
 
h18< 0.1%
 
k4< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I46654161.1%
 
B11270014.8%
 
G8889611.6%
 
S456316.0%
 
P456316.0%
 
O13220.2%
 
R9490.1%
 
F7820.1%
 
L355< 0.1%
 
E355< 0.1%
 
U4< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/45631100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2441100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin718805499.3%
 
Common480720.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i114275915.9%
 
d93151813.0%
 
n83180411.6%
 
u6258218.7%
 
v5546557.7%
 
l5131217.1%
 
a5053447.0%
 
I4665416.5%
 
s3398314.7%
 
e3378004.7%
 
t1381951.9%
 
o1366171.9%
 
B1127001.6%
 
c923991.3%
 
r902181.3%
 
m892511.2%
 
G888961.2%
 
S456310.6%
 
y456310.6%
 
P456310.6%
 
b456310.6%
 
g1898< 0.1%
 
O1322< 0.1%
 
f1065< 0.1%
 
R949< 0.1%
 
Other values (8)2826< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
/4563194.9%
 
24415.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII7236126100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i114275915.8%
 
d93151812.9%
 
n83180411.5%
 
u6258218.6%
 
v5546557.7%
 
l5131217.1%
 
a5053447.0%
 
I4665416.4%
 
s3398314.7%
 
e3378004.7%
 
t1381951.9%
 
o1366171.9%
 
B1127001.6%
 
c923991.3%
 
r902181.2%
 
m892511.2%
 
G888961.2%
 
S456310.6%
 
y456310.6%
 
/456310.6%
 
P456310.6%
 
b456310.6%
 
2441< 0.1%
 
g1898< 0.1%
 
O1322< 0.1%
 
Other values (10)48400.1%
 

Apartment Number
Categorical

HIGH CARDINALITY
MISSING

Distinct11491
Distinct (%)7.0%
Missing587135
Missing (%)78.2%
Memory size5.7 MiB
100
 
1906
A
 
1627
101
 
1601
102
 
1424
103
 
1272
Other values (11486)
155897 
ValueCountFrequency (%) 
10019060.3%
 
A16270.2%
 
10116010.2%
 
10214240.2%
 
10312720.2%
 
B12720.2%
 
10411790.2%
 
11011340.2%
 
OFFICE10010.1%
 
1059230.1%
 
1068930.1%
 
2018680.1%
 
1088540.1%
 
2038490.1%
 
C8400.1%
 
OFC8240.1%
 
1097970.1%
 
2027830.1%
 
1077710.1%
 
2047660.1%
 
1117460.1%
 
2007320.1%
 
D7140.1%
 
1206830.1%
 
1126780.1%
 
Other values (11466)13859018.5%
 
(Missing)58713578.2%
 
2021-03-12T16:39:29.276271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5874 ?
Unique (%)3.6%
2021-03-12T16:39:29.501442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length3
Mean length3.094932225
Min length1

Overview of Unicode Properties

Unique unicode characters70
Unique unicode categories13 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n117427150.5%
 
a58713525.3%
 
11312815.6%
 
0900773.9%
 
2864003.7%
 
3496892.1%
 
4370391.6%
 
5302741.3%
 
6252941.1%
 
7225521.0%
 
8212250.9%
 
9172580.7%
 
A51160.2%
 
C42520.2%
 
F42400.2%
 
E41790.2%
 
B40510.2%
 
O35860.2%
 
33780.1%
 
D26890.1%
 
I21740.1%
 
T20570.1%
 
S19910.1%
 
R18570.1%
 
L18400.1%
 
Other values (45)99620.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter176144275.8%
 
Decimal Number51108922.0%
 
Uppercase Letter458682.0%
 
Space Separator33780.1%
 
Dash Punctuation1061< 0.1%
 
Other Punctuation831< 0.1%
 
Math Symbol96< 0.1%
 
Modifier Symbol68< 0.1%
 
Connector Punctuation17< 0.1%
 
Open Punctuation12< 0.1%
 
Other Symbol2< 0.1%
 
Control2< 0.1%
 
Close Punctuation1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
113128125.7%
 
09007717.6%
 
28640016.9%
 
3496899.7%
 
4370397.2%
 
5302745.9%
 
6252944.9%
 
7225524.4%
 
8212254.2%
 
9172583.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n117427166.7%
 
a58713533.3%
 
c9< 0.1%
 
o6< 0.1%
 
e4< 0.1%
 
d4< 0.1%
 
l3< 0.1%
 
k3< 0.1%
 
r3< 0.1%
 
x2< 0.1%
 
w1< 0.1%
 
b1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A511611.2%
 
C42529.3%
 
F42409.2%
 
E41799.1%
 
B40518.8%
 
O35867.8%
 
D26895.9%
 
I21744.7%
 
T20574.5%
 
S19914.3%
 
R18574.0%
 
L18404.0%
 
N15213.3%
 
G10732.3%
 
P10542.3%
 
U7741.7%
 
H6981.5%
 
M6331.4%
 
Y5101.1%
 
K4731.0%
 
W4090.9%
 
J2710.6%
 
V1780.4%
 
X1550.3%
 
Q560.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
#53764.6%
 
.9811.8%
 
/9811.8%
 
,455.4%
 
&192.3%
 
*91.1%
 
?91.1%
 
\50.6%
 
'50.6%
 
"40.5%
 
:20.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3378100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1061100.0%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`68100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
=9295.8%
 
+33.1%
 
~11.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(12100.0%
 

Most frequent Other Symbol characters

ValueCountFrequency (%) 
�2100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)1100.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_17100.0%
 

Most frequent Control characters

ValueCountFrequency (%) 
2100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin180731077.8%
 
Common51655722.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
113128125.4%
 
09007717.4%
 
28640016.7%
 
3496899.6%
 
4370397.2%
 
5302745.9%
 
6252944.9%
 
7225524.4%
 
8212254.1%
 
9172583.3%
 
33780.7%
 
-10610.2%
 
#5370.1%
 
.98< 0.1%
 
/98< 0.1%
 
=92< 0.1%
 
`68< 0.1%
 
,45< 0.1%
 
&19< 0.1%
 
_17< 0.1%
 
(12< 0.1%
 
*9< 0.1%
 
?9< 0.1%
 
\5< 0.1%
 
'5< 0.1%
 
Other values (7)15< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n117427165.0%
 
a58713532.5%
 
A51160.3%
 
C42520.2%
 
F42400.2%
 
E41790.2%
 
B40510.2%
 
O35860.2%
 
D26890.1%
 
I21740.1%
 
T20570.1%
 
S19910.1%
 
R18570.1%
 
L18400.1%
 
N15210.1%
 
G10730.1%
 
P10540.1%
 
U774< 0.1%
 
H698< 0.1%
 
M633< 0.1%
 
Y510< 0.1%
 
K473< 0.1%
 
W409< 0.1%
 
J271< 0.1%
 
V178< 0.1%
 
Other values (13)278< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2323865> 99.9%
 
Specials2< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n117427150.5%
 
a58713525.3%
 
11312815.6%
 
0900773.9%
 
2864003.7%
 
3496892.1%
 
4370391.6%
 
5302741.3%
 
6252941.1%
 
7225521.0%
 
8212250.9%
 
9172580.7%
 
A51160.2%
 
C42520.2%
 
F42400.2%
 
E41790.2%
 
B40510.2%
 
O35860.2%
 
33780.1%
 
D26890.1%
 
I21740.1%
 
T20570.1%
 
S19910.1%
 
R18570.1%
 
L18400.1%
 
Other values (44)99600.4%
 

Most frequent Specials characters

ValueCountFrequency (%) 
�2100.0%
 

Victim Race
Categorical

MISSING

Distinct12
Distinct (%)< 0.1%
Missing277119
Missing (%)36.9%
Memory size5.7 MiB
Black
159887 
Hispanic or Latino
146214 
White
144011 
Asian
 
8327
Middle Eastern
 
6160
Other values (7)
 
9144
ValueCountFrequency (%) 
Black15988721.3%
 
Hispanic or Latino14621419.5%
 
White14401119.2%
 
Asian83271.1%
 
Middle Eastern61600.8%
 
Unknown44810.6%
 
Non-Hispanic or Latino33800.5%
 
American Indian or Alaska Native6660.1%
 
Native Hawaiian/Pacific Islander5610.1%
 
NH44< 0.1%
 
TEST8< 0.1%
 
H4< 0.1%
 
(Missing)27711936.9%
 
2021-03-12T16:39:29.713521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:29.877887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length5
Mean length6.999540528
Min length1

Overview of Unicode Properties

Unique unicode characters34
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n88785616.9%
 
a75737714.4%
 
i61208311.6%
 
c3112695.9%
 
3091345.9%
 
o3077155.9%
 
t3009925.7%
 
l1672743.2%
 
s1653083.1%
 
k1650343.1%
 
B1598873.0%
 
e1587853.0%
 
r1576473.0%
 
H1502032.9%
 
p1495942.8%
 
L1495942.8%
 
W1440112.7%
 
h1440112.7%
 
d135470.3%
 
A96590.2%
 
E61680.1%
 
M61600.1%
 
w50420.1%
 
N46510.1%
 
U44810.1%
 
Other values (9)82070.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter430598881.9%
 
Uppercase Letter63662612.1%
 
Space Separator3091345.9%
 
Dash Punctuation33800.1%
 
Other Punctuation561< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n88785620.6%
 
a75737717.6%
 
i61208314.2%
 
c3112697.2%
 
o3077157.1%
 
t3009927.0%
 
l1672743.9%
 
s1653083.8%
 
k1650343.8%
 
e1587853.7%
 
r1576473.7%
 
p1495943.5%
 
h1440113.3%
 
d135470.3%
 
w50420.1%
 
v1227< 0.1%
 
m666< 0.1%
 
f561< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B15988725.1%
 
H15020323.6%
 
L14959423.5%
 
W14401122.6%
 
A96591.5%
 
E61681.0%
 
M61601.0%
 
N46510.7%
 
U44810.7%
 
I12270.2%
 
P5610.1%
 
T16< 0.1%
 
S8< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
309134100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-3380100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/561100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin494261494.0%
 
Common3130756.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n88785618.0%
 
a75737715.3%
 
i61208312.4%
 
c3112696.3%
 
o3077156.2%
 
t3009926.1%
 
l1672743.4%
 
s1653083.3%
 
k1650343.3%
 
B1598873.2%
 
e1587853.2%
 
r1576473.2%
 
H1502033.0%
 
p1495943.0%
 
L1495943.0%
 
W1440112.9%
 
h1440112.9%
 
d135470.3%
 
A96590.2%
 
E61680.1%
 
M61600.1%
 
w50420.1%
 
N46510.1%
 
U44810.1%
 
I1227< 0.1%
 
Other values (6)30390.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
30913498.7%
 
-33801.1%
 
/5610.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5255689100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n88785616.9%
 
a75737714.4%
 
i61208311.6%
 
c3112695.9%
 
3091345.9%
 
o3077155.9%
 
t3009925.7%
 
l1672743.2%
 
s1653083.1%
 
k1650343.1%
 
B1598873.0%
 
e1587853.0%
 
r1576473.0%
 
H1502032.9%
 
p1495942.8%
 
L1495942.8%
 
W1440112.7%
 
h1440112.7%
 
d135470.3%
 
A96590.2%
 
E61680.1%
 
M61600.1%
 
w50420.1%
 
N46510.1%
 
U44810.1%
 
Other values (9)82070.2%
 

Reporting Area
Real number (ℝ≥0)

MISSING

Distinct1152
Distinct (%)0.2%
Missing18639
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean3142.572428
Minimum1001
Maximum9611
Zeros0
Zeros (%)0.0%
Memory size5.7 MiB
2021-03-12T16:39:30.241851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1031
Q11247
median3059
Q34321
95-th percentile6038
Maximum9611
Range8610
Interquartile range (IQR)3074

Descriptive statistics

Standard deviation1824.635377
Coefficient of variation (CV)0.5806184006
Kurtosis1.781977793
Mean3142.572428
Median Absolute Deviation (MAD)1289
Skewness1.068225437
Sum2301063811
Variance3329294.258
MonotocityNot monotonic
2021-03-12T16:39:30.646841image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
121767650.9%
 
212355470.7%
 
436051920.7%
 
104247250.6%
 
103046440.6%
 
108444960.6%
 
118041590.6%
 
305940780.5%
 
440337400.5%
 
100836610.5%
 
442336460.5%
 
305835920.5%
 
438135710.5%
 
600632240.4%
 
102730920.4%
 
113230840.4%
 
302530210.4%
 
442429970.4%
 
601229760.4%
 
102228110.4%
 
455227880.4%
 
124627530.4%
 
100927420.4%
 
117726460.4%
 
203826240.3%
 
Other values (1127)63964985.2%
 
(Missing)186392.5%
 
ValueCountFrequency (%) 
10017540.1%
 
1002158< 0.1%
 
10036060.1%
 
100415240.2%
 
1005299< 0.1%
 
100611330.2%
 
10075730.1%
 
100836610.5%
 
100927420.4%
 
10107560.1%
 
ValueCountFrequency (%) 
96119200.1%
 
961023810.3%
 
96095070.1%
 
96089020.1%
 
960713490.2%
 
9606352< 0.1%
 
960511420.2%
 
960423680.3%
 
96037770.1%
 
96028760.1%
 

Victim Home Address
Categorical

HIGH CARDINALITY
MISSING

Distinct272530
Distinct (%)38.6%
Missing44510
Missing (%)5.9%
Memory size5.7 MiB
725 N JIM MILLER RD
 
14770
9915 E NORTHWEST HWY
 
13302
9801 HARRY HINES BLVD
 
11838
334 S HALL ST
 
11241
1999 E CAMP WISDOM RD
 
10647
Other values (272525)
644554 
ValueCountFrequency (%) 
725 N JIM MILLER RD147702.0%
 
9915 E NORTHWEST HWY133021.8%
 
9801 HARRY HINES BLVD118381.6%
 
334 S HALL ST112411.5%
 
1999 E CAMP WISDOM RD106471.4%
 
1400 S LAMAR ST100401.3%
 
4230 W ILLINOIS AVE92801.2%
 
1500 MARILLA ST74611.0%
 
6969 MCCALLUM BLVD73181.0%
 
1818 CORSICANA ST31550.4%
 
1400 S LAMAR16260.2%
 
1600 CHESTNUT ST12780.2%
 
777 N GALLOWAY AVE11640.2%
 
HOMELESS11100.1%
 
725 N JIM MILLER10900.1%
 
4230 W ILLINOIS9550.1%
 
1500 MARILLA8450.1%
 
1500 MARILLA STREEET8400.1%
 
9801 HARRY HINES8290.1%
 
1551 BAYLOR ST7830.1%
 
1891 FOREST LN7690.1%
 
1616 WOODALL RODGERS FWY7060.1%
 
6969 MCCALLUM6650.1%
 
8687 N CENTRAL SERV SB6560.1%
 
8687 N CENTRAL EXPY6520.1%
 
Other values (272505)59333279.0%
 
(Missing)445105.9%
 
2021-03-12T16:39:32.384585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique202729 ?
Unique (%)28.7%
2021-03-12T16:39:32.611317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length58
Median length17
Mean length16.33214492
Min length1

Overview of Unicode Properties

Unique unicode characters57
Unique unicode categories11 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
182732614.9%
 
R7436506.1%
 
E6860335.6%
 
A6527465.3%
 
L6067884.9%
 
15176264.2%
 
N4880594.0%
 
S4834283.9%
 
D4801123.9%
 
T4533203.7%
 
04360423.6%
 
O3947463.2%
 
I3829893.1%
 
23524002.9%
 
33097432.5%
 
52756922.2%
 
92467542.0%
 
M2436692.0%
 
42403662.0%
 
H2242301.8%
 
C2140861.7%
 
W2140651.7%
 
V2105731.7%
 
71810751.5%
 
61806041.5%
 
Other values (32)12170659.9%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter738484660.2%
 
Decimal Number291038523.7%
 
Space Separator182732614.9%
 
Lowercase Letter1335301.1%
 
Other Punctuation5904< 0.1%
 
Dash Punctuation1130< 0.1%
 
Modifier Symbol23< 0.1%
 
Open Punctuation20< 0.1%
 
Close Punctuation19< 0.1%
 
Other Symbol2< 0.1%
 
Math Symbol2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n8902066.7%
 
a4451033.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
151762617.8%
 
043604215.0%
 
235240012.1%
 
330974310.6%
 
52756929.5%
 
92467548.5%
 
42403668.3%
 
71810756.2%
 
61806046.2%
 
81700835.8%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1827326100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R74365010.1%
 
E6860339.3%
 
A6527468.8%
 
L6067888.2%
 
N4880596.6%
 
S4834286.5%
 
D4801126.5%
 
T4533206.1%
 
O3947465.3%
 
I3829895.2%
 
M2436693.3%
 
H2242303.0%
 
C2140862.9%
 
W2140652.9%
 
V2105732.9%
 
B1522362.1%
 
P1502952.0%
 
Y1476512.0%
 
G1113231.5%
 
K1104901.5%
 
U1023091.4%
 
F727461.0%
 
J356070.5%
 
X136870.2%
 
Z77830.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.502185.0%
 
#3395.7%
 
&2253.8%
 
'1332.3%
 
/1021.7%
 
,671.1%
 
"60.1%
 
?50.1%
 
;30.1%
 
:1< 0.1%
 
@1< 0.1%
 
\1< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1130100.0%
 

Most frequent Other Symbol characters

ValueCountFrequency (%) 
�2100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
=2100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(20100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)19100.0%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`23100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin751837661.3%
 
Common474481138.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R7436509.9%
 
E6860339.1%
 
A6527468.7%
 
L6067888.1%
 
N4880596.5%
 
S4834286.4%
 
D4801126.4%
 
T4533206.0%
 
O3947465.3%
 
I3829895.1%
 
M2436693.2%
 
H2242303.0%
 
C2140862.8%
 
W2140652.8%
 
V2105732.8%
 
B1522362.0%
 
P1502952.0%
 
Y1476512.0%
 
G1113231.5%
 
K1104901.5%
 
U1023091.4%
 
n890201.2%
 
F727461.0%
 
a445100.6%
 
J356070.5%
 
Other values (3)236950.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
182732638.5%
 
151762610.9%
 
04360429.2%
 
23524007.4%
 
33097436.5%
 
52756925.8%
 
92467545.2%
 
42403665.1%
 
71810753.8%
 
61806043.8%
 
81700833.6%
 
.50210.1%
 
-1130< 0.1%
 
#339< 0.1%
 
&225< 0.1%
 
'133< 0.1%
 
/102< 0.1%
 
,67< 0.1%
 
`23< 0.1%
 
(20< 0.1%
 
)19< 0.1%
 
"6< 0.1%
 
?5< 0.1%
 
;3< 0.1%
 
�2< 0.1%
 
Other values (4)5< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII12263185> 99.9%
 
Specials2< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
182732614.9%
 
R7436506.1%
 
E6860335.6%
 
A6527465.3%
 
L6067884.9%
 
15176264.2%
 
N4880594.0%
 
S4834283.9%
 
D4801123.9%
 
T4533203.7%
 
04360423.6%
 
O3947463.2%
 
I3829893.1%
 
23524002.9%
 
33097432.5%
 
52756922.2%
 
92467542.0%
 
M2436692.0%
 
42403662.0%
 
H2242301.8%
 
C2140861.7%
 
W2140651.7%
 
V2105731.7%
 
71810751.5%
 
61806041.5%
 
Other values (31)12170639.9%
 

Most frequent Specials characters

ValueCountFrequency (%) 
�2100.0%
 

Beat
Real number (ℝ≥0)

HIGH CORRELATION

Distinct237
Distinct (%)< 0.1%
Missing316
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean415.9782572
Minimum3
Maximum757
Zeros0
Zeros (%)0.0%
Memory size5.7 MiB
2021-03-12T16:39:32.807560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile131
Q1238
median421
Q3553
95-th percentile742
Maximum757
Range754
Interquartile range (IQR)315

Descriptive statistics

Standard deviation196.7872677
Coefficient of variation (CV)0.4730710423
Kurtosis-1.152895886
Mean415.9782572
Median Absolute Deviation (MAD)178
Skewness0.1531459002
Sum312210817
Variance38725.22872
MonotocityNot monotonic
2021-03-12T16:39:32.979361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
52178371.0%
 
15372831.0%
 
31869730.9%
 
53467470.9%
 
52367310.9%
 
25260780.8%
 
13260260.8%
 
15460030.8%
 
61459440.8%
 
41658560.8%
 
14357020.8%
 
63556930.8%
 
13156020.7%
 
74454520.7%
 
51351300.7%
 
13550980.7%
 
13449650.7%
 
45449400.7%
 
11547270.6%
 
65447020.6%
 
23346920.6%
 
15146740.6%
 
63246240.6%
 
55245700.6%
 
52245500.6%
 
Other values (212)60994781.2%
 
ValueCountFrequency (%) 
32< 0.1%
 
71< 0.1%
 
141< 0.1%
 
11124530.3%
 
11227070.4%
 
11326980.4%
 
11435270.5%
 
11547270.6%
 
11617940.2%
 
12138680.5%
 
ValueCountFrequency (%) 
75717760.2%
 
75630110.4%
 
75522620.3%
 
75422080.3%
 
75321770.3%
 
75222670.3%
 
75127860.4%
 
74826060.3%
 
74724870.3%
 
74621410.3%
 

Victim Apartment
Categorical

HIGH CARDINALITY
MISSING

Distinct11828
Distinct (%)5.8%
Missing545670
Missing (%)72.7%
Memory size5.7 MiB
101
 
1752
A
 
1731
102
 
1600
B
 
1558
103
 
1515
Other values (11823)
197036 
ValueCountFrequency (%) 
10117520.2%
 
A17310.2%
 
10216000.2%
 
B15580.2%
 
10315150.2%
 
10013550.2%
 
10412920.2%
 
10511280.2%
 
10611100.1%
 
C11050.1%
 
20110660.1%
 
20310630.1%
 
11010590.1%
 
20210550.1%
 
2049870.1%
 
1089810.1%
 
1079370.1%
 
D9310.1%
 
5008730.1%
 
1098420.1%
 
2068040.1%
 
2077860.1%
 
1117820.1%
 
1127500.1%
 
2007480.1%
 
Other values (11803)17738223.6%
 
(Missing)54567072.7%
 
2021-03-12T16:39:33.239511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5639 ?
Unique (%)2.7%
2021-03-12T16:39:33.422109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length3
Mean length3.108651124
Min length1

Overview of Unicode Properties

Unique unicode characters64
Unique unicode categories11 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n109134246.8%
 
a54567023.4%
 
11671617.2%
 
21137644.9%
 
01128084.8%
 
3660612.8%
 
4479032.1%
 
5395641.7%
 
6336511.4%
 
7294831.3%
 
8272271.2%
 
9216880.9%
 
A48980.2%
 
B35330.2%
 
C33250.1%
 
E30910.1%
 
F24750.1%
 
23930.1%
 
D23140.1%
 
T18430.1%
 
O17270.1%
 
L12600.1%
 
N12420.1%
 
S12270.1%
 
I11730.1%
 
Other values (39)73450.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter163701770.1%
 
Decimal Number65931028.2%
 
Uppercase Letter334511.4%
 
Space Separator23930.1%
 
Dash Punctuation1138< 0.1%
 
Other Punctuation742< 0.1%
 
Modifier Symbol67< 0.1%
 
Math Symbol32< 0.1%
 
Connector Punctuation11< 0.1%
 
Open Punctuation5< 0.1%
 
Close Punctuation2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n109134266.7%
 
a54567033.3%
 
e2< 0.1%
 
c1< 0.1%
 
x1< 0.1%
 
d1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
116716125.4%
 
211376417.3%
 
011280817.1%
 
36606110.0%
 
4479037.3%
 
5395646.0%
 
6336515.1%
 
7294834.5%
 
8272274.1%
 
9216883.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A489814.6%
 
B353310.6%
 
C33259.9%
 
E30919.2%
 
F24757.4%
 
D23146.9%
 
T18435.5%
 
O17275.2%
 
L12603.8%
 
N12423.7%
 
S12273.7%
 
I11733.5%
 
P10973.3%
 
R9742.9%
 
G6321.9%
 
H6241.9%
 
U4861.5%
 
M3591.1%
 
W3030.9%
 
K2510.8%
 
J2450.7%
 
Y1540.5%
 
Q840.3%
 
V810.2%
 
X410.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
#44860.4%
 
.14519.5%
 
/8010.8%
 
,263.5%
 
*131.8%
 
?81.1%
 
&60.8%
 
\60.8%
 
"40.5%
 
:30.4%
 
;10.1%
 
'10.1%
 
!10.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2393100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1138100.0%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`67100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
=2990.6%
 
~26.2%
 
+13.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_11100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(5100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)2100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin167046871.6%
 
Common66370028.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n109134265.3%
 
a54567032.7%
 
A48980.3%
 
B35330.2%
 
C33250.2%
 
E30910.2%
 
F24750.1%
 
D23140.1%
 
T18430.1%
 
O17270.1%
 
L12600.1%
 
N12420.1%
 
S12270.1%
 
I11730.1%
 
P10970.1%
 
R9740.1%
 
G632< 0.1%
 
H624< 0.1%
 
U486< 0.1%
 
M359< 0.1%
 
W303< 0.1%
 
K251< 0.1%
 
J245< 0.1%
 
Y154< 0.1%
 
Q84< 0.1%
 
Other values (7)139< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
116716125.2%
 
211376417.1%
 
011280817.0%
 
36606110.0%
 
4479037.2%
 
5395646.0%
 
6336515.1%
 
7294834.4%
 
8272274.1%
 
9216883.3%
 
23930.4%
 
-11380.2%
 
#4480.1%
 
.145< 0.1%
 
/80< 0.1%
 
`67< 0.1%
 
=29< 0.1%
 
,26< 0.1%
 
*13< 0.1%
 
_11< 0.1%
 
?8< 0.1%
 
&6< 0.1%
 
\6< 0.1%
 
(5< 0.1%
 
"4< 0.1%
 
Other values (7)11< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2334168100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n109134246.8%
 
a54567023.4%
 
11671617.2%
 
21137644.9%
 
01128084.8%
 
3660612.8%
 
4479032.1%
 
5395641.7%
 
6336511.4%
 
7294831.3%
 
8272271.2%
 
9216880.9%
 
A48980.2%
 
B35330.2%
 
C33250.1%
 
E30910.1%
 
F24750.1%
 
23930.1%
 
D23140.1%
 
T18430.1%
 
O17270.1%
 
L12600.1%
 
N12420.1%
 
S12270.1%
 
I11730.1%
 
Other values (39)73450.3%
 

Division
Categorical

Distinct14
Distinct (%)< 0.1%
Missing316
Missing (%)< 0.1%
Memory size5.7 MiB
NORTHEAST
68720 
CENTRAL
63377 
NORTHWEST
61624 
SOUTHWEST
60533 
SOUTHEAST
59443 
Other values (9)
436849 
ValueCountFrequency (%) 
NORTHEAST687209.2%
 
CENTRAL633778.4%
 
NORTHWEST616248.2%
 
SOUTHWEST605338.1%
 
SOUTHEAST594437.9%
 
NorthEast567657.6%
 
SOUTH CENTRAL538407.2%
 
SouthEast535047.1%
 
SouthWest522287.0%
 
NorthWest486526.5%
 
Central479946.4%
 
South Central446936.0%
 
NORTH CENTRAL445975.9%
 
North Central345764.6%
 
(Missing)316< 0.1%
 
2021-03-12T16:39:33.601108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:33.762142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length9
Mean length9.647503802
Min length3

Overview of Unicode Properties

Unique unicode characters23
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
T76089110.5%
 
t6288308.7%
 
S5745617.9%
 
E5224037.2%
 
N4767486.6%
 
O3487574.8%
 
H3487574.8%
 
R3367554.6%
 
o2904184.0%
 
h2904184.0%
 
A2899774.0%
 
C2890774.0%
 
r2672563.7%
 
a2378483.3%
 
e2281433.1%
 
W2230373.1%
 
s2111492.9%
 
1777062.5%
 
U1738162.4%
 
L1618142.2%
 
u1504252.1%
 
n1278951.8%
 
l1272631.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter450659362.2%
 
Lowercase Letter255964535.3%
 
Space Separator1777062.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T76089116.9%
 
S57456112.7%
 
E52240311.6%
 
N47674810.6%
 
O3487577.7%
 
H3487577.7%
 
R3367557.5%
 
A2899776.4%
 
C2890776.4%
 
W2230374.9%
 
U1738163.9%
 
L1618143.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
177706100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t62883024.6%
 
o29041811.3%
 
h29041811.3%
 
r26725610.4%
 
a2378489.3%
 
e2281438.9%
 
s2111498.2%
 
u1504255.9%
 
n1278955.0%
 
l1272635.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin706623897.5%
 
Common1777062.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
T76089110.8%
 
t6288308.9%
 
S5745618.1%
 
E5224037.4%
 
N4767486.7%
 
O3487574.9%
 
H3487574.9%
 
R3367554.8%
 
o2904184.1%
 
h2904184.1%
 
A2899774.1%
 
C2890774.1%
 
r2672563.8%
 
a2378483.4%
 
e2281433.2%
 
W2230373.2%
 
s2111493.0%
 
U1738162.5%
 
L1618142.3%
 
u1504252.1%
 
n1278951.8%
 
l1272631.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
177706100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII7243944100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
T76089110.5%
 
t6288308.7%
 
S5745617.9%
 
E5224037.2%
 
N4767486.6%
 
O3487574.8%
 
H3487574.8%
 
R3367554.6%
 
o2904184.0%
 
h2904184.0%
 
A2899774.0%
 
C2890774.0%
 
r2672563.7%
 
a2378483.3%
 
e2281433.1%
 
W2230373.1%
 
s2111492.9%
 
1777062.5%
 
U1738162.4%
 
L1618142.2%
 
u1504252.1%
 
n1278951.8%
 
l1272631.8%
 

Sector
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38
Distinct (%)< 0.1%
Missing804
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean411.8617894
Minimum0
Maximum750
Zeros286
Zeros (%)< 0.1%
Memory size5.7 MiB
2021-03-12T16:39:33.880723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile130
Q1230
median420
Q3550
95-th percentile740
Maximum750
Range750
Interquartile range (IQR)320

Descriptive statistics

Standard deviation197.0111971
Coefficient of variation (CV)0.4783429835
Kurtosis-1.157794699
Mean411.8617894
Median Absolute Deviation (MAD)180
Skewness0.1537937699
Sum308920230
Variance38813.41179
MonotocityNot monotonic
2021-03-12T16:39:34.012266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%) 
530279793.7%
 
250276023.7%
 
130272143.6%
 
150269763.6%
 
210267303.6%
 
520266573.6%
 
220258663.4%
 
230253793.4%
 
740253393.4%
 
410247383.3%
 
310242503.2%
 
440239253.2%
 
430236913.2%
 
330236833.2%
 
350224093.0%
 
450221472.9%
 
320220912.9%
 
510214652.9%
 
720206632.8%
 
340204302.7%
 
140203522.7%
 
730200112.7%
 
240197922.6%
 
120188482.5%
 
420181312.4%
 
Other values (13)16369021.8%
 
ValueCountFrequency (%) 
0286< 0.1%
 
302< 0.1%
 
701< 0.1%
 
110178832.4%
 
120188482.5%
 
130272143.6%
 
140203522.7%
 
150269763.6%
 
210267303.6%
 
220258663.4%
 
ValueCountFrequency (%) 
750164792.2%
 
740253393.4%
 
730200112.7%
 
720206632.8%
 
710159772.1%
 
650133301.8%
 
640152592.0%
 
630176772.4%
 
620159882.1%
 
610167972.2%
 

Victim City
Categorical

HIGH CARDINALITY
MISSING

Distinct6439
Distinct (%)0.9%
Missing45112
Missing (%)6.0%
Memory size5.7 MiB
DALLAS
584627 
GARLAND
 
7981
MESQUITE
 
7281
IRVING
 
6962
PLANO
 
4822
Other values (6434)
94077 
ValueCountFrequency (%) 
DALLAS58462777.9%
 
GARLAND79811.1%
 
MESQUITE72811.0%
 
IRVING69620.9%
 
PLANO48220.6%
 
ARLINGTON43100.6%
 
RICHARDSON32740.4%
 
GRAND PRAIRIE32300.4%
 
CARROLLTON30000.4%
 
FORT WORTH28770.4%
 
DESOTO27870.4%
 
LANCASTER26400.4%
 
Dallas23570.3%
 
CEDAR HILL17820.2%
 
DUNCANVILLE17420.2%
 
LEWISVILLE17290.2%
 
FRISCO16890.2%
 
MCKINNEY16270.2%
 
HOUSTON13900.2%
 
ROWLETT13280.2%
 
FARMERS BRANCH11480.2%
 
FORNEY10820.1%
 
ALLEN10500.1%
 
ROCKWALL10060.1%
 
BALCH SPRINGS9570.1%
 
Other values (6414)530727.1%
 
(Missing)451126.0%
 
2021-03-12T16:39:34.235197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3613 ?
Unique (%)0.5%
2021-03-12T16:39:34.405059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length6
Mean length6.18003175
Min length1

Overview of Unicode Properties

Unique unicode characters51
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A126247827.2%
 
L125143627.0%
 
S63287113.6%
 
D62394013.4%
 
R933322.0%
 
n902241.9%
 
N871581.9%
 
E791411.7%
 
I759751.6%
 
O688511.5%
 
T545681.2%
 
a498261.1%
 
C335750.7%
 
G328590.7%
 
H281110.6%
 
238900.5%
 
U215030.5%
 
M196530.4%
 
P189120.4%
 
V177130.4%
 
W153310.3%
 
F133060.3%
 
Y98850.2%
 
K95770.2%
 
B85410.2%
 
Other values (26)176950.4%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter446885096.3%
 
Lowercase Letter1471213.2%
 
Space Separator238900.5%
 
Other Punctuation395< 0.1%
 
Decimal Number64< 0.1%
 
Dash Punctuation24< 0.1%
 
Modifier Symbol5< 0.1%
 
Math Symbol1< 0.1%
 
Other Symbol1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n9022461.3%
 
a4982633.9%
 
l47143.2%
 
s23571.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A126247828.3%
 
L125143628.0%
 
S63287114.2%
 
D62394014.0%
 
R933322.1%
 
N871582.0%
 
E791411.8%
 
I759751.7%
 
O688511.5%
 
T545681.2%
 
C335750.8%
 
G328590.7%
 
H281110.6%
 
U215030.5%
 
M196530.4%
 
P189120.4%
 
V177130.4%
 
W153310.3%
 
F133060.3%
 
Y98850.2%
 
K95770.2%
 
B85410.2%
 
Q79640.2%
 
X1400< 0.1%
 
J561< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
23890100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.30376.7%
 
,7519.0%
 
'133.3%
 
;20.5%
 
#10.3%
 
/10.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
01117.2%
 
11015.6%
 
2914.1%
 
5710.9%
 
769.4%
 
369.4%
 
657.8%
 
957.8%
 
434.7%
 
823.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-24100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
=1100.0%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`5100.0%
 

Most frequent Other Symbol characters

ValueCountFrequency (%) 
�1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin461597199.5%
 
Common243800.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A126247827.4%
 
L125143627.1%
 
S63287113.7%
 
D62394013.5%
 
R933322.0%
 
n902242.0%
 
N871581.9%
 
E791411.7%
 
I759751.6%
 
O688511.5%
 
T545681.2%
 
a498261.1%
 
C335750.7%
 
G328590.7%
 
H281110.6%
 
U215030.5%
 
M196530.4%
 
P189120.4%
 
V177130.4%
 
W153310.3%
 
F133060.3%
 
Y98850.2%
 
K95770.2%
 
B85410.2%
 
Q79640.2%
 
Other values (5)92410.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
2389098.0%
 
.3031.2%
 
,750.3%
 
-240.1%
 
'130.1%
 
011< 0.1%
 
110< 0.1%
 
29< 0.1%
 
57< 0.1%
 
76< 0.1%
 
36< 0.1%
 
65< 0.1%
 
95< 0.1%
 
`5< 0.1%
 
43< 0.1%
 
;2< 0.1%
 
82< 0.1%
 
=1< 0.1%
 
#1< 0.1%
 
�1< 0.1%
 
/1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4640350> 99.9%
 
Specials1< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A126247827.2%
 
L125143627.0%
 
S63287113.6%
 
D62394013.4%
 
R933322.0%
 
n902241.9%
 
N871581.9%
 
E791411.7%
 
I759751.6%
 
O688511.5%
 
T545681.2%
 
a498261.1%
 
C335750.7%
 
G328590.7%
 
H281110.6%
 
238900.5%
 
U215030.5%
 
M196530.4%
 
P189120.4%
 
V177130.4%
 
W153310.3%
 
F133060.3%
 
Y98850.2%
 
K95770.2%
 
B85410.2%
 
Other values (25)176940.4%
 

Most frequent Specials characters

ValueCountFrequency (%) 
�1100.0%
 

Council District
Categorical

MISSING

Distinct19
Distinct (%)< 0.1%
Missing273214
Missing (%)36.4%
Memory size5.7 MiB
D2
49054 
D6
43362 
D14
40735 
D7
39393 
D8
34514 
Other values (14)
270590 
ValueCountFrequency (%) 
D2490546.5%
 
D6433625.8%
 
D14407355.4%
 
D7393935.2%
 
D8345144.6%
 
D4317554.2%
 
D10249573.3%
 
D11245323.3%
 
D3237383.2%
 
D13233463.1%
 
D1208272.8%
 
D9206732.8%
 
D5203462.7%
 
8181982.4%
 
D12149012.0%
 
11133061.8%
 
10132361.8%
 
1108371.4%
 
999381.3%
 
(Missing)27321436.4%
 
2021-03-12T16:39:34.576884image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:34.757779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.483060802
Min length1

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n54642829.3%
 
D41213322.1%
 
a27321414.7%
 
122451512.0%
 
4724903.9%
 
2639553.4%
 
8527122.8%
 
3470842.5%
 
6433622.3%
 
7393932.1%
 
0381932.0%
 
9306111.6%
 
5203461.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter81964244.0%
 
Decimal Number63266133.9%
 
Uppercase Letter41213322.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D412133100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
122451535.5%
 
47249011.5%
 
26395510.1%
 
8527128.3%
 
3470847.4%
 
6433626.9%
 
7393936.2%
 
0381936.0%
 
9306114.8%
 
5203463.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n54642866.7%
 
a27321433.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin123177566.1%
 
Common63266133.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n54642844.4%
 
D41213333.5%
 
a27321422.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
122451535.5%
 
47249011.5%
 
26395510.1%
 
8527128.3%
 
3470847.4%
 
6433626.9%
 
7393936.2%
 
0381936.0%
 
9306114.8%
 
5203463.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1864436100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n54642829.3%
 
D41213322.1%
 
a27321414.7%
 
122451512.0%
 
4724903.9%
 
2639553.4%
 
8527122.8%
 
3470842.5%
 
6433622.3%
 
7393932.1%
 
0381932.0%
 
9306111.6%
 
5203461.1%
 

Victim State
Categorical

HIGH CARDINALITY
MISSING

Distinct82
Distinct (%)< 0.1%
Missing50304
Missing (%)6.7%
Memory size5.7 MiB
TX
686007 
OK
 
1717
LA
 
1154
CA
 
1153
AR
 
952
Other values (77)
 
9575
ValueCountFrequency (%) 
TX68600791.4%
 
OK17170.2%
 
LA11540.2%
 
CA11530.2%
 
AR9520.1%
 
FL9120.1%
 
IL6630.1%
 
TN6450.1%
 
GA6120.1%
 
MO4740.1%
 
T4270.1%
 
AZ4090.1%
 
CO3850.1%
 
MS331< 0.1%
 
KS302< 0.1%
 
NC284< 0.1%
 
OH270< 0.1%
 
NY270< 0.1%
 
MI253< 0.1%
 
PA216< 0.1%
 
NM208< 0.1%
 
IN205< 0.1%
 
VA202< 0.1%
 
AL200< 0.1%
 
NJ184< 0.1%
 
Other values (57)21230.3%
 
(Missing)503046.7%
 
2021-03-12T16:39:34.952817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique11 ?
Unique (%)< 0.1%
2021-03-12T16:39:35.145845image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.066422325
Min length1

Overview of Unicode Properties

Unique unicode characters28
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
T68733344.3%
 
X68604044.2%
 
n1006086.5%
 
a503043.2%
 
A52910.3%
 
O29580.2%
 
L29310.2%
 
N22550.1%
 
K21630.1%
 
C20980.1%
 
M17700.1%
 
I14420.1%
 
R10720.1%
 
F9120.1%
 
S8220.1%
 
G612< 0.1%
 
Y462< 0.1%
 
Z410< 0.1%
 
W387< 0.1%
 
V358< 0.1%
 
D356< 0.1%
 
H299< 0.1%
 
P230< 0.1%
 
J184< 0.1%
 
U144< 0.1%
 
Other values (3)157< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter140068690.3%
 
Lowercase Letter1509129.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n10060866.7%
 
a5030433.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T68733349.1%
 
X68604049.0%
 
A52910.4%
 
O29580.2%
 
L29310.2%
 
N22550.2%
 
K21630.2%
 
C20980.1%
 
M17700.1%
 
I14420.1%
 
R10720.1%
 
F9120.1%
 
S8220.1%
 
G612< 0.1%
 
Y462< 0.1%
 
Z410< 0.1%
 
W387< 0.1%
 
V358< 0.1%
 
D356< 0.1%
 
H299< 0.1%
 
P230< 0.1%
 
J184< 0.1%
 
U144< 0.1%
 
E94< 0.1%
 
B52< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1551598100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
T68733344.3%
 
X68604044.2%
 
n1006086.5%
 
a503043.2%
 
A52910.3%
 
O29580.2%
 
L29310.2%
 
N22550.1%
 
K21630.1%
 
C20980.1%
 
M17700.1%
 
I14420.1%
 
R10720.1%
 
F9120.1%
 
S8220.1%
 
G612< 0.1%
 
Y462< 0.1%
 
Z410< 0.1%
 
W387< 0.1%
 
V358< 0.1%
 
D356< 0.1%
 
H299< 0.1%
 
P230< 0.1%
 
J184< 0.1%
 
U144< 0.1%
 
Other values (3)157< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1551598100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
T68733344.3%
 
X68604044.2%
 
n1006086.5%
 
a503043.2%
 
A52910.3%
 
O29580.2%
 
L29310.2%
 
N22550.1%
 
K21630.1%
 
C20980.1%
 
M17700.1%
 
I14420.1%
 
R10720.1%
 
F9120.1%
 
S8220.1%
 
G612< 0.1%
 
Y462< 0.1%
 
Z410< 0.1%
 
W387< 0.1%
 
V358< 0.1%
 
D356< 0.1%
 
H299< 0.1%
 
P230< 0.1%
 
J184< 0.1%
 
U144< 0.1%
 
Other values (3)157< 0.1%
 

Target Area Action Grids
Categorical

MISSING

Distinct31
Distinct (%)< 0.1%
Missing511352
Missing (%)68.1%
Memory size5.7 MiB
Ross Bennett
17770 
WebbChapel Timberline
 
15238
Forest Audelia
 
14853
Five Points
 
14782
CampWisdom Chaucer
 
13481
Other values (26)
163386 
ValueCountFrequency (%) 
Ross Bennett177702.4%
 
WebbChapel Timberline152382.0%
 
Forest Audelia148532.0%
 
Five Points147822.0%
 
CampWisdom Chaucer134811.8%
 
SpringValley Preston112261.5%
 
Wycliff Lemmon100211.3%
 
LakeJune Buckner99281.3%
 
StAugustine Bruton97481.3%
 
Jefferson Corridor92871.2%
 
Greenville LBJ86371.2%
 
Monument GoodLatimer85931.1%
 
JuliusSchepps Central84851.1%
 
JohnWest Buckner82551.1%
 
Hatcher Scyene81291.1%
 
NWHwy WaltonWalker80901.1%
 
Loop12 JimMiller70170.9%
 
WalnutHill Jupiter63300.8%
 
McKinney Allen56780.8%
 
Overton Illinois54290.7%
 
Forest Dennis51050.7%
 
CampWisdom Westmoreland44050.6%
 
Buckner 3043940.6%
 
Coit Springvalley41260.5%
 
John Carpenter Stemmons35590.5%
 
Other values (6)169442.3%
 
(Missing)51135268.1%
 
2021-03-12T16:39:35.336050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:35.517918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length3
Mean length7.281175502
Min length3

Overview of Unicode Properties

Unique unicode characters47
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n132862024.3%
 
a66802012.2%
 
e4589418.4%
 
2465114.5%
 
r2339544.3%
 
i2332524.3%
 
o2291084.2%
 
t2285704.2%
 
l2067243.8%
 
s1778683.3%
 
u1376872.5%
 
m1121892.1%
 
W912731.7%
 
p857941.6%
 
c765001.4%
 
C748061.4%
 
h627901.1%
 
B621741.1%
 
J614981.1%
 
d601881.1%
 
S580961.1%
 
y504650.9%
 
b489090.9%
 
L473910.9%
 
k432900.8%
 
Other values (22)3825407.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter456337183.5%
 
Uppercase Letter63445411.6%
 
Space Separator2465114.5%
 
Decimal Number228220.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n132862029.1%
 
a66802014.6%
 
e45894110.1%
 
r2339545.1%
 
i2332525.1%
 
o2291085.0%
 
t2285705.0%
 
l2067244.5%
 
s1778683.9%
 
u1376873.0%
 
m1121892.5%
 
p857941.9%
 
c765001.7%
 
h627901.4%
 
d601881.3%
 
y504651.1%
 
b489091.1%
 
k432900.9%
 
v425100.9%
 
f386160.8%
 
g251000.6%
 
w142760.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
W9127314.4%
 
C7480611.8%
 
B621749.8%
 
J614989.7%
 
S580969.2%
 
L473917.5%
 
F376395.9%
 
A302794.8%
 
P287034.5%
 
H225493.6%
 
M212883.4%
 
R177702.8%
 
G172302.7%
 
T152382.4%
 
V112261.8%
 
K103421.6%
 
N80901.3%
 
D80041.3%
 
O54290.9%
 
I54290.9%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
246511100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1701730.7%
 
2701730.7%
 
3439419.3%
 
0439419.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin519782595.1%
 
Common2693334.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n132862025.6%
 
a66802012.9%
 
e4589418.8%
 
r2339544.5%
 
i2332524.5%
 
o2291084.4%
 
t2285704.4%
 
l2067244.0%
 
s1778683.4%
 
u1376872.6%
 
m1121892.2%
 
W912731.8%
 
p857941.7%
 
c765001.5%
 
C748061.4%
 
h627901.2%
 
B621741.2%
 
J614981.2%
 
d601881.2%
 
S580961.1%
 
y504651.0%
 
b489090.9%
 
L473910.9%
 
k432900.8%
 
v425100.8%
 
Other values (17)3172086.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
24651191.5%
 
170172.6%
 
270172.6%
 
343941.6%
 
043941.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5467158100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n132862024.3%
 
a66802012.2%
 
e4589418.4%
 
2465114.5%
 
r2339544.3%
 
i2332524.3%
 
o2291084.2%
 
t2285704.2%
 
l2067243.8%
 
s1778683.3%
 
u1376872.5%
 
m1121892.1%
 
W912731.7%
 
p857941.6%
 
c765001.4%
 
C748061.4%
 
h627901.1%
 
B621741.1%
 
J614981.1%
 
d601881.1%
 
S580961.1%
 
y504650.9%
 
b489090.9%
 
L473910.9%
 
k432900.8%
 
Other values (22)3825407.0%
 

Victim Business Name
Categorical

HIGH CARDINALITY
MISSING

Distinct15910
Distinct (%)43.4%
Missing714181
Missing (%)95.1%
Memory size5.7 MiB
SELF
 
986
UNEMPLOYED
 
867
CITY OF DALLAS
 
714
SELF EMPLOYED
 
650
7-11
 
328
Other values (15905)
33136 
ValueCountFrequency (%) 
SELF9860.1%
 
UNEMPLOYED8670.1%
 
CITY OF DALLAS7140.1%
 
SELF EMPLOYED6500.1%
 
7-11328< 0.1%
 
RETIRED303< 0.1%
 
WALMART297< 0.1%
 
NONE244< 0.1%
 
FAMILY DOLLAR216< 0.1%
 
UNKNOWN200< 0.1%
 
CVS189< 0.1%
 
LOWES184< 0.1%
 
LOWE'S158< 0.1%
 
KROGER158< 0.1%
 
DALLAS POLICE DEPARTMENT156< 0.1%
 
TOM THUMB152< 0.1%
 
SMU138< 0.1%
 
WALGREENS130< 0.1%
 
MCDONALDS123< 0.1%
 
AT&T119< 0.1%
 
JACK IN THE BOX118< 0.1%
 
UBER117< 0.1%
 
7 ELEVEN112< 0.1%
 
UPS111< 0.1%
 
SOUTHWEST AIRLINES108< 0.1%
 
Other values (15885)298034.0%
 
(Missing)71418195.1%
 
2021-03-12T16:39:35.758353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique11577 ?
Unique (%)31.6%
2021-03-12T16:39:35.982022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length3
Mean length3.549204781
Min length1

Overview of Unicode Properties

Unique unicode characters65
Unique unicode categories13 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n142836253.6%
 
a71418126.8%
 
E493981.9%
 
463401.7%
 
A420881.6%
 
S347201.3%
 
O336881.3%
 
T328421.2%
 
L323181.2%
 
R317881.2%
 
N294211.1%
 
I284671.1%
 
C225870.8%
 
D169190.6%
 
M152960.6%
 
P136770.5%
 
U132910.5%
 
H130920.5%
 
Y99360.4%
 
G86480.3%
 
F85930.3%
 
B69290.3%
 
W57680.2%
 
K53870.2%
 
V48030.2%
 
Other values (40)164240.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter214254380.4%
 
Uppercase Letter46482017.4%
 
Space Separator463401.7%
 
Decimal Number53970.2%
 
Other Punctuation44130.2%
 
Dash Punctuation1206< 0.1%
 
Open Punctuation94< 0.1%
 
Close Punctuation89< 0.1%
 
Other Symbol27< 0.1%
 
Math Symbol24< 0.1%
 
Modifier Symbol6< 0.1%
 
Connector Punctuation3< 0.1%
 
Currency Symbol1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n142836266.7%
 
a71418133.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E4939810.6%
 
A420889.1%
 
S347207.5%
 
O336887.2%
 
T328427.1%
 
L323187.0%
 
R317886.8%
 
N294216.3%
 
I284676.1%
 
C225874.9%
 
D169193.6%
 
M152963.3%
 
P136772.9%
 
U132912.9%
 
H130922.8%
 
Y99362.1%
 
G86481.9%
 
F85931.8%
 
B69291.5%
 
W57681.2%
 
K53871.2%
 
V48031.0%
 
X19520.4%
 
Z13940.3%
 
J12090.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
46340100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1206100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'135030.6%
 
.105623.9%
 
&76017.2%
 
,44010.0%
 
/3708.4%
 
#2706.1%
 
@611.4%
 
:330.7%
 
*320.7%
 
"280.6%
 
!60.1%
 
?40.1%
 
\2< 0.1%
 
;1< 0.1%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(9297.9%
 
[22.1%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)8898.9%
 
]11.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1147627.3%
 
798018.2%
 
056810.5%
 
25179.6%
 
93706.9%
 
33376.2%
 
53195.9%
 
43195.9%
 
62765.1%
 
82354.4%
 

Most frequent Other Symbol characters

ValueCountFrequency (%) 
�27100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+2291.7%
 
~14.2%
 
=14.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_3100.0%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`6100.0%
 

Most frequent Currency Symbol characters

ValueCountFrequency (%) 
$1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin260736397.8%
 
Common576002.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n142836254.8%
 
a71418127.4%
 
E493981.9%
 
A420881.6%
 
S347201.3%
 
O336881.3%
 
T328421.3%
 
L323181.2%
 
R317881.2%
 
N294211.1%
 
I284671.1%
 
C225870.9%
 
D169190.6%
 
M152960.6%
 
P136770.5%
 
U132910.5%
 
H130920.5%
 
Y99360.4%
 
G86480.3%
 
F85930.3%
 
B69290.3%
 
W57680.2%
 
K53870.2%
 
V48030.2%
 
X19520.1%
 
Other values (3)32120.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
4634080.5%
 
114762.6%
 
'13502.3%
 
-12062.1%
 
.10561.8%
 
79801.7%
 
&7601.3%
 
05681.0%
 
25170.9%
 
,4400.8%
 
/3700.6%
 
93700.6%
 
33370.6%
 
53190.6%
 
43190.6%
 
62760.5%
 
#2700.5%
 
82350.4%
 
(920.2%
 
)880.2%
 
@610.1%
 
:330.1%
 
*320.1%
 
"28< 0.1%
 
�27< 0.1%
 
Other values (12)500.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2664936> 99.9%
 
Specials27< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n142836253.6%
 
a71418126.8%
 
E493981.9%
 
463401.7%
 
A420881.6%
 
S347201.3%
 
O336881.3%
 
T328421.2%
 
L323181.2%
 
R317881.2%
 
N294211.1%
 
I284671.1%
 
C225870.8%
 
D169190.6%
 
M152960.6%
 
P136770.5%
 
U132910.5%
 
H130920.5%
 
Y99360.4%
 
G86480.3%
 
F85930.3%
 
B69290.3%
 
W57680.2%
 
K53870.2%
 
V48030.2%
 
Other values (39)163970.6%
 

Most frequent Specials characters

ValueCountFrequency (%) 
�27100.0%
 

Community
Categorical

MISSING

Distinct27
Distinct (%)< 0.1%
Missing670559
Missing (%)89.3%
Memory size5.7 MiB
Northwest_PFA
13784 
ForestAudelia_PFA
9282 
Chaucer_PFA
7771 
KitMaham_PFA
6369 
FergusonWoodmeadow_PFA
4311 
Other values (22)
38786 
ValueCountFrequency (%) 
Northwest_PFA137841.8%
 
ForestAudelia_PFA92821.2%
 
Chaucer_PFA77711.0%
 
KitMaham_PFA63690.8%
 
FergusonWoodmeadow_PFA43110.6%
 
Malcolm_PFA41360.6%
 
MLK_PFA34650.5%
 
BryanHenderson_PFA34570.5%
 
KiestPolk_PFA32920.4%
 
FivePoints_PFA26840.4%
 
Whitehurst_PFA26230.3%
 
OvertonIllinois_PFA24820.3%
 
PeakColumbia_PFA24000.3%
 
Bachman Lake_PFA23540.3%
 
McCallumCoit_PFA20110.3%
 
Maple_PFA17140.2%
 
LakeCliff_PFA15200.2%
 
East South_PFA15130.2%
 
Vickery Meadows_PFA13590.2%
 
Barbara Jordan_PFA11310.2%
 
Red Cloud_PFA6880.1%
 
Garrett Park PFA6660.1%
 
PembertonHill_PFA6310.1%
 
Forest Heights4520.1%
 
Vermont Village_PFA145< 0.1%
 
Other values (2)63< 0.1%
 
(Missing)67055989.3%
 
2021-03-12T16:39:36.231731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:36.636418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length3
Mean length4.198763554
Min length3

Overview of Unicode Properties

Unique unicode characters44
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n136775743.4%
 
a73826423.4%
 
F965803.1%
 
P895242.8%
 
A891332.8%
 
e813632.6%
 
_791852.5%
 
o691732.2%
 
t650202.1%
 
r551451.7%
 
s457061.4%
 
i404791.3%
 
l377061.2%
 
h374891.2%
 
u306471.0%
 
d252270.8%
 
m223570.7%
 
w194690.6%
 
M190540.6%
 
c176310.6%
 
C164640.5%
 
N137840.4%
 
K131260.4%
 
k115910.4%
 
90220.3%
 
Other values (19)617962.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter268887885.3%
 
Uppercase Letter37560711.9%
 
Connector Punctuation791852.5%
 
Space Separator90220.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F9658025.7%
 
P8952423.8%
 
A8913323.7%
 
M190545.1%
 
C164644.4%
 
N137843.7%
 
K131263.5%
 
L73392.0%
 
B69421.8%
 
W69341.8%
 
H45401.2%
 
O24820.7%
 
I24820.7%
 
V16640.4%
 
E15130.4%
 
S15130.4%
 
J11310.3%
 
R6880.2%
 
G6660.2%
 
Q48< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n136775750.9%
 
a73826427.5%
 
e813633.0%
 
o691732.6%
 
t650202.4%
 
r551452.1%
 
s457061.7%
 
i404791.5%
 
l377061.4%
 
h374891.4%
 
u306471.1%
 
d252270.9%
 
m223570.8%
 
w194690.7%
 
c176310.7%
 
k115910.4%
 
v51660.2%
 
g49080.2%
 
y48640.2%
 
b41620.2%
 
f30400.1%
 
p17140.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_79185100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
9022100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin306448597.2%
 
Common882072.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n136775744.6%
 
a73826424.1%
 
F965803.2%
 
P895242.9%
 
A891332.9%
 
e813632.7%
 
o691732.3%
 
t650202.1%
 
r551451.8%
 
s457061.5%
 
i404791.3%
 
l377061.2%
 
h374891.2%
 
u306471.0%
 
d252270.8%
 
m223570.7%
 
w194690.6%
 
M190540.6%
 
c176310.6%
 
C164640.5%
 
N137840.4%
 
K131260.4%
 
k115910.4%
 
L73390.2%
 
B69420.2%
 
Other values (17)475151.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
_7918589.8%
 
902210.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3152692100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n136775743.4%
 
a73826423.4%
 
F965803.1%
 
P895242.8%
 
A891332.8%
 
e813632.6%
 
_791852.5%
 
o691732.2%
 
t650202.1%
 
r551451.7%
 
s457061.4%
 
i404791.3%
 
l377061.2%
 
h374891.2%
 
u306471.0%
 
d252270.8%
 
m223570.7%
 
w194690.6%
 
M190540.6%
 
c176310.6%
 
C164640.5%
 
N137840.4%
 
K131260.4%
 
k115910.4%
 
90220.3%
 
Other values (19)617962.0%
 

Victim Business Address
Categorical

HIGH CARDINALITY
MISSING

Distinct13471
Distinct (%)50.9%
Missing724416
Missing (%)96.5%
Memory size5.7 MiB
1710 CHALK HILL RD
 
169
1500 MARILLA ST
 
89
8687 N CENTRAL EXPY
 
87
9915 E NORTHWEST HWY
 
85
1400 S LAMAR ST
 
78
Other values (13466)
25938 
ValueCountFrequency (%) 
1710 CHALK HILL RD169< 0.1%
 
1500 MARILLA ST89< 0.1%
 
8687 N CENTRAL EXPY87< 0.1%
 
9915 E NORTHWEST HWY85< 0.1%
 
1400 S LAMAR ST78< 0.1%
 
8008 HERB KELLEHER WAY76< 0.1%
 
6011 LEMMON AVE73< 0.1%
 
334 S HALL ST62< 0.1%
 
10155 MONROE DR59< 0.1%
 
160 CONTINENTAL AVE55< 0.1%
 
6004 SAMUELL BLVD53< 0.1%
 
8687 N CENTRAL SERV SB49< 0.1%
 
725 N JIM MILLER RD43< 0.1%
 
6011 LEMMON AVE42< 0.1%
 
7777 FOREST LN42< 0.1%
 
1999 E CAMP WISDOM RD41< 0.1%
 
1818 N WESTMORELAND RD40< 0.1%
 
13350 DALLAS PKWY40< 0.1%
 
205 S LAMAR ST39< 0.1%
 
9801 HARRY HINES BLVD39< 0.1%
 
655 W ILLINOIS AVE39< 0.1%
 
6333 E MOCKINGBIRD LN38< 0.1%
 
5500 GREENVILLE AVE37< 0.1%
 
5050 S LANCASTER RD37< 0.1%
 
1521 N COCKRELL HILL RD36< 0.1%
 
Other values (13446)249983.3%
 
(Missing)72441696.5%
 
2021-03-12T16:39:36.906017image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9007 ?
Unique (%)34.1%
2021-03-12T16:39:37.127992image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length3
Mean length3.511565374
Min length3

Overview of Unicode Properties

Unique unicode characters50
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n144883254.9%
 
a72441627.5%
 
727622.8%
 
E274581.0%
 
R272711.0%
 
L231260.9%
 
A222030.8%
 
N221890.8%
 
1207870.8%
 
0198930.8%
 
S180410.7%
 
T172680.7%
 
D163540.6%
 
O144300.5%
 
2133810.5%
 
I124320.5%
 
5110340.4%
 
3104970.4%
 
W89470.3%
 
V89030.3%
 
M84260.3%
 
482360.3%
 
C77670.3%
 
H77350.3%
 
B67610.3%
 
Other values (25)575522.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter217324882.4%
 
Uppercase Letter28079610.6%
 
Decimal Number1093844.1%
 
Space Separator727622.8%
 
Other Punctuation457< 0.1%
 
Dash Punctuation49< 0.1%
 
Open Punctuation2< 0.1%
 
Close Punctuation2< 0.1%
 
Math Symbol1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n144883266.7%
 
a72441633.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
12078719.0%
 
01989318.2%
 
21338112.2%
 
51103410.1%
 
3104979.6%
 
482367.5%
 
766326.1%
 
865226.0%
 
963425.8%
 
660605.5%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
72762100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E274589.8%
 
R272719.7%
 
L231268.2%
 
A222037.9%
 
N221897.9%
 
S180416.4%
 
T172686.1%
 
D163545.8%
 
O144305.1%
 
I124324.4%
 
W89473.2%
 
V89033.2%
 
M84263.0%
 
C77672.8%
 
H77352.8%
 
B67612.4%
 
Y64572.3%
 
P61352.2%
 
K50771.8%
 
G40251.4%
 
F38651.4%
 
U34421.2%
 
J11670.4%
 
X9750.3%
 
Z2700.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.33874.0%
 
/5311.6%
 
;255.5%
 
&224.8%
 
,92.0%
 
'71.5%
 
#30.7%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-49100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(2100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)2100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin245404493.1%
 
Common1826576.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n144883259.0%
 
a72441629.5%
 
E274581.1%
 
R272711.1%
 
L231260.9%
 
A222030.9%
 
N221890.9%
 
S180410.7%
 
T172680.7%
 
D163540.7%
 
O144300.6%
 
I124320.5%
 
W89470.4%
 
V89030.4%
 
M84260.3%
 
C77670.3%
 
H77350.3%
 
B67610.3%
 
Y64570.3%
 
P61350.2%
 
K50770.2%
 
G40250.2%
 
F38650.2%
 
U34420.1%
 
J1167< 0.1%
 
Other values (3)13170.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
7276239.8%
 
12078711.4%
 
01989310.9%
 
2133817.3%
 
5110346.0%
 
3104975.7%
 
482364.5%
 
766323.6%
 
865223.6%
 
963423.5%
 
660603.3%
 
.3380.2%
 
/53< 0.1%
 
-49< 0.1%
 
;25< 0.1%
 
&22< 0.1%
 
,9< 0.1%
 
'7< 0.1%
 
#3< 0.1%
 
(2< 0.1%
 
)2< 0.1%
 
+1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2636701100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n144883254.9%
 
a72441627.5%
 
727622.8%
 
E274581.0%
 
R272711.0%
 
L231260.9%
 
A222030.8%
 
N221890.8%
 
1207870.8%
 
0198930.8%
 
S180410.7%
 
T172680.7%
 
D163540.6%
 
O144300.5%
 
2133810.5%
 
I124320.5%
 
5110340.4%
 
3104970.4%
 
W89470.3%
 
V89030.3%
 
M84260.3%
 
482360.3%
 
C77670.3%
 
H77350.3%
 
B67610.3%
 
Other values (25)575522.2%
 

Victim Business Phone
Categorical

HIGH CARDINALITY
MISSING

Distinct7238
Distinct (%)68.9%
Missing740352
Missing (%)98.6%
Memory size5.7 MiB
2146704415
 
68
2146704413
 
45
2146708345
 
37
2142590021
 
36
2146714500
 
34
Other values (7233)
10290 
ValueCountFrequency (%) 
214670441568< 0.1%
 
214670441345< 0.1%
 
214670834537< 0.1%
 
214259002136< 0.1%
 
214671450034< 0.1%
 
972892205027< 0.1%
 
214670617824< 0.1%
 
214670747024< 0.1%
 
972246144723< 0.1%
 
214426626221< 0.1%
 
214670725321< 0.1%
 
214750624919< 0.1%
 
972662055917< 0.1%
 
972387866417< 0.1%
 
972702005515< 0.1%
 
469249928814< 0.1%
 
214670680012< 0.1%
 
214428020212< 0.1%
 
972656250111< 0.1%
 
972437132311< 0.1%
 
214849683111< 0.1%
 
214229299611< 0.1%
 
214741115110< 0.1%
 
21429513009< 0.1%
 
97270936039< 0.1%
 
Other values (7213)99721.3%
 
(Missing)74035298.6%
 
2021-03-12T16:39:37.379103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5574 ?
Unique (%)53.0%
2021-03-12T16:39:37.564057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length3
Mean length3.097963407
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n148070463.7%
 
a74035231.8%
 
2167140.7%
 
4147210.6%
 
1131410.6%
 
799750.4%
 
999720.4%
 
096850.4%
 
384310.4%
 
683470.4%
 
871810.3%
 
569100.3%
 
10< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter222105695.5%
 
Decimal Number1050774.5%
 
Space Separator10< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n148070466.7%
 
a74035233.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
21671415.9%
 
41472114.0%
 
11314112.5%
 
799759.5%
 
999729.5%
 
096859.2%
 
384318.0%
 
683477.9%
 
871816.8%
 
569106.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
10100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin222105695.5%
 
Common1050874.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n148070466.7%
 
a74035233.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
21671415.9%
 
41472114.0%
 
11314112.5%
 
799759.5%
 
999729.5%
 
096859.2%
 
384318.0%
 
683477.9%
 
871816.8%
 
569106.6%
 
10< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2326143100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n148070463.7%
 
a74035231.8%
 
2167140.7%
 
4147210.6%
 
1131410.6%
 
799750.4%
 
999720.4%
 
096850.4%
 
384310.4%
 
683470.4%
 
871810.3%
 
569100.3%
 
10< 0.1%
 

Year1 of Occurrence
Real number (ℝ≥0)

HIGH CORRELATION

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.532363
Minimum1900
Maximum2021
Zeros0
Zeros (%)0.0%
Memory size5.7 MiB
2021-03-12T16:39:37.742491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile2014
Q12016
median2018
Q32019
95-th percentile2020
Maximum2021
Range121
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.03958669
Coefficient of variation (CV)0.001010931337
Kurtosis143.450271
Mean2017.532363
Median Absolute Deviation (MAD)2
Skewness-2.801161016
Sum1514888385
Variance4.159913864
MonotocityNot monotonic
2021-03-12T16:39:37.880708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%) 
202013323217.7%
 
201912988517.3%
 
201812046816.0%
 
201610015913.3%
 
20179518912.7%
 
20159350112.5%
 
2014571097.6%
 
2021207902.8%
 
2013232< 0.1%
 
201269< 0.1%
 
201148< 0.1%
 
201032< 0.1%
 
200024< 0.1%
 
200822< 0.1%
 
200919< 0.1%
 
200718< 0.1%
 
19009< 0.1%
 
20048< 0.1%
 
20027< 0.1%
 
20066< 0.1%
 
20055< 0.1%
 
20015< 0.1%
 
19973< 0.1%
 
19893< 0.1%
 
20033< 0.1%
 
Other values (12)16< 0.1%
 
ValueCountFrequency (%) 
19009< 0.1%
 
19081< 0.1%
 
19741< 0.1%
 
19751< 0.1%
 
19801< 0.1%
 
19881< 0.1%
 
19893< 0.1%
 
19901< 0.1%
 
19911< 0.1%
 
19922< 0.1%
 
ValueCountFrequency (%) 
2021207902.8%
 
202013323217.7%
 
201912988517.3%
 
201812046816.0%
 
20179518912.7%
 
201610015913.3%
 
20159350112.5%
 
2014571097.6%
 
2013232< 0.1%
 
201269< 0.1%
 

Month1 of Occurence
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
July
70714 
August
69851 
October
67192 
September
66053 
December
65532 
Other values (7)
411520 
ValueCountFrequency (%) 
July707149.4%
 
August698519.3%
 
October671928.9%
 
September660538.8%
 
December655328.7%
 
January646058.6%
 
June644248.6%
 
November630788.4%
 
May568337.6%
 
February561387.5%
 
March541197.2%
 
April523237.0%
 
2021-03-12T16:39:38.071553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:38.225603image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length7
Mean length6.202746976
Min length3

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e70866515.2%
 
r54517811.7%
 
u3955838.5%
 
b3179936.8%
 
a2963006.4%
 
y2482905.3%
 
t2030964.4%
 
J1997434.3%
 
m1946634.2%
 
c1868434.0%
 
o1302702.8%
 
n1290292.8%
 
l1230372.6%
 
A1221742.6%
 
p1183762.5%
 
M1109522.4%
 
g698511.5%
 
s698511.5%
 
O671921.4%
 
S660531.4%
 
D655321.4%
 
N630781.4%
 
v630781.4%
 
F561381.2%
 
h541191.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter390654583.9%
 
Uppercase Letter75086216.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J19974326.6%
 
A12217416.3%
 
M11095214.8%
 
O671928.9%
 
S660538.8%
 
D655328.7%
 
N630788.4%
 
F561387.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e70866518.1%
 
r54517814.0%
 
u39558310.1%
 
b3179938.1%
 
a2963007.6%
 
y2482906.4%
 
t2030965.2%
 
m1946635.0%
 
c1868434.8%
 
o1302703.3%
 
n1290293.3%
 
l1230373.1%
 
p1183763.0%
 
g698511.8%
 
s698511.8%
 
v630781.6%
 
h541191.4%
 
i523231.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4657407100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e70866515.2%
 
r54517811.7%
 
u3955838.5%
 
b3179936.8%
 
a2963006.4%
 
y2482905.3%
 
t2030964.4%
 
J1997434.3%
 
m1946634.2%
 
c1868434.0%
 
o1302702.8%
 
n1290292.8%
 
l1230372.6%
 
A1221742.6%
 
p1183762.5%
 
M1109522.4%
 
g698511.5%
 
s698511.5%
 
O671921.4%
 
S660531.4%
 
D655321.4%
 
N630781.4%
 
v630781.4%
 
F561381.2%
 
h541191.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4657407100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e70866515.2%
 
r54517811.7%
 
u3955838.5%
 
b3179936.8%
 
a2963006.4%
 
y2482905.3%
 
t2030964.4%
 
J1997434.3%
 
m1946634.2%
 
c1868434.0%
 
o1302702.8%
 
n1290292.8%
 
l1230372.6%
 
A1221742.6%
 
p1183762.5%
 
M1109522.4%
 
g698511.5%
 
s698511.5%
 
O671921.4%
 
S660531.4%
 
D655321.4%
 
N630781.4%
 
v630781.4%
 
F561381.2%
 
h541191.2%
 

Responding Officer #1 Badge No
Categorical

HIGH CARDINALITY
MISSING

Distinct4304
Distinct (%)0.6%
Missing30186
Missing (%)4.0%
Memory size5.7 MiB
94392
 
9573
118918
 
4251
106291
 
3509
120365
 
3315
6751
 
1903
Other values (4299)
698125 
ValueCountFrequency (%) 
9439295731.3%
 
11891842510.6%
 
10629135090.5%
 
12036533150.4%
 
675119030.3%
 
316213960.2%
 
712813810.2%
 
873512190.2%
 
797012010.2%
 
970411910.2%
 
991211870.2%
 
869011410.2%
 
708011010.1%
 
740210720.1%
 
1066710620.1%
 
934710520.1%
 
771210440.1%
 
1051010430.1%
 
758710320.1%
 
758410230.1%
 
1051510070.1%
 
902610020.1%
 
96549960.1%
 
76839900.1%
 
87519670.1%
 
Other values (4279)67601890.0%
 
(Missing)301864.0%
 
2021-03-12T16:39:38.435631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique225 ?
Unique (%)< 0.1%
2021-03-12T16:39:38.588763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length5
Mean length4.489048587
Min length3

Overview of Unicode Properties

Unique unicode characters23
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
174479822.1%
 
041249812.2%
 
934519710.2%
 
82978668.8%
 
72781018.3%
 
62696038.0%
 
52428847.2%
 
22277986.8%
 
42272236.7%
 
32272196.7%
 
n603721.8%
 
a301860.9%
 
M24600.1%
 
F1101< 0.1%
 
T915< 0.1%
 
U817< 0.1%
 
P817< 0.1%
 
R487< 0.1%
 
D281< 0.1%
 
13< 0.1%
 
W10< 0.1%
 
-7< 0.1%
 
E3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number327318797.1%
 
Lowercase Letter905582.7%
 
Uppercase Letter68910.2%
 
Space Separator13< 0.1%
 
Dash Punctuation7< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
174479822.8%
 
041249812.6%
 
934519710.5%
 
82978669.1%
 
72781018.5%
 
62696038.2%
 
52428847.4%
 
22277987.0%
 
42272236.9%
 
32272196.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n6037266.7%
 
a3018633.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M246035.7%
 
F110116.0%
 
T91513.3%
 
U81711.9%
 
P81711.9%
 
R4877.1%
 
D2814.1%
 
W100.1%
 
E3< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-7100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
13100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common327320797.1%
 
Latin974492.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
174479822.8%
 
041249812.6%
 
934519710.5%
 
82978669.1%
 
72781018.5%
 
62696038.2%
 
52428847.4%
 
22277987.0%
 
42272236.9%
 
32272196.9%
 
13< 0.1%
 
-7< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n6037262.0%
 
a3018631.0%
 
M24602.5%
 
F11011.1%
 
T9150.9%
 
U8170.8%
 
P8170.8%
 
R4870.5%
 
D2810.3%
 
W10< 0.1%
 
E3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3370656100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
174479822.1%
 
041249812.2%
 
934519710.2%
 
82978668.8%
 
72781018.3%
 
62696038.0%
 
52428847.2%
 
22277986.8%
 
42272236.7%
 
32272196.7%
 
n603721.8%
 
a301860.9%
 
M24600.1%
 
F1101< 0.1%
 
T915< 0.1%
 
U817< 0.1%
 
P817< 0.1%
 
R487< 0.1%
 
D281< 0.1%
 
13< 0.1%
 
W10< 0.1%
 
-7< 0.1%
 
E3< 0.1%
 

Day1 of the Week
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
Fri
114093 
Sat
111150 
Sun
107224 
Mon
106964 
Thu
105018 
Other values (2)
206413 
ValueCountFrequency (%) 
Fri11409315.2%
 
Sat11115014.8%
 
Sun10722414.3%
 
Mon10696414.2%
 
Thu10501814.0%
 
Wed10340413.8%
 
Tue10300913.7%
 
2021-03-12T16:39:38.739013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:38.842450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:39:39.035176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
u31525114.0%
 
S2183749.7%
 
n2141889.5%
 
T2080279.2%
 
e2064139.2%
 
F1140935.1%
 
r1140935.1%
 
i1140935.1%
 
a1111504.9%
 
t1111504.9%
 
M1069644.7%
 
o1069644.7%
 
h1050184.7%
 
W1034044.6%
 
d1034044.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter150172466.7%
 
Uppercase Letter75086233.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S21837429.1%
 
T20802727.7%
 
F11409315.2%
 
M10696414.2%
 
W10340413.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
u31525121.0%
 
n21418814.3%
 
e20641313.7%
 
r1140937.6%
 
i1140937.6%
 
a1111507.4%
 
t1111507.4%
 
o1069647.1%
 
h1050187.0%
 
d1034046.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2252586100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
u31525114.0%
 
S2183749.7%
 
n2141889.5%
 
T2080279.2%
 
e2064139.2%
 
F1140935.1%
 
r1140935.1%
 
i1140935.1%
 
a1111504.9%
 
t1111504.9%
 
M1069644.7%
 
o1069644.7%
 
h1050184.7%
 
W1034044.6%
 
d1034044.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2252586100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
u31525114.0%
 
S2183749.7%
 
n2141889.5%
 
T2080279.2%
 
e2064139.2%
 
F1140935.1%
 
r1140935.1%
 
i1140935.1%
 
a1111504.9%
 
t1111504.9%
 
M1069644.7%
 
o1069644.7%
 
h1050184.7%
 
W1034044.6%
 
d1034044.6%
 

Responding Officer #1 Name
Categorical

HIGH CARDINALITY
MISSING

Distinct4252
Distinct (%)0.6%
Missing31010
Missing (%)4.1%
Memory size5.7 MiB
WILLIS,LINDA,M
 
9573
SPURR,RUTH
 
4251
BELAYE,DIANE,KAY
 
3509
BURNETT,MICHELLE,J
 
3315
CAMPOPIANO III,PAUL,PASQUELE
 
1903
Other values (4247)
697301 
ValueCountFrequency (%) 
WILLIS,LINDA,M95731.3%
 
SPURR,RUTH42510.6%
 
BELAYE,DIANE,KAY35090.5%
 
BURNETT,MICHELLE,J33150.4%
 
CAMPOPIANO III,PAUL,PASQUELE19030.3%
 
MONCIBAIS,FERNANDO13960.2%
 
TREIGLE JR,FRANKLIN,STEVEN13810.2%
 
PILLSBURY,TODD,A12190.2%
 
MARTINEZ JR,AGUSTIN12010.2%
 
STUARD,JC11910.2%
 
NAUMAN,REX,VIRGIL11870.2%
 
STREETER,MARK,K11410.2%
 
MCLACHLAN,MARK,ANTHONY11010.1%
 
ORTIZ JR,FIDEL10720.1%
 
ROSS,TIMOTHY10620.1%
 
NEVAREZ,FRANCISCO,JAVIER10520.1%
 
HEBERT,HEATHER,RENAE10440.1%
 
VINSON,DARIO10430.1%
 
CRAIG,DOUGLAS,JAMES10320.1%
 
NOWAK-CHACON,SHELBY,ANN10230.1%
 
CUDDY,JESSICA,ROBIN10070.1%
 
FORD,PAUL,CHRISTIAN10020.1%
 
BANDAS,WAYI,ALIBEY9960.1%
 
MCCLENON,SUSAN,ARLEEN9900.1%
 
WINGFIELD,VICTOR,LYNN9670.1%
 
Other values (4227)67519489.9%
 
(Missing)310104.1%
 
2021-03-12T16:39:39.231368image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique224 ?
Unique (%)< 0.1%
2021-03-12T16:39:39.407613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length17
Mean length16.73438794
Min length1

Overview of Unicode Properties

Unique unicode characters35
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A127225310.1%
 
E11883029.5%
 
,11831869.4%
 
R9845617.8%
 
N9144407.3%
 
I7561376.0%
 
L7259955.8%
 
O7094395.6%
 
S5982144.8%
 
T5015374.0%
 
H4427813.5%
 
D4165533.3%
 
C3917493.1%
 
M3705942.9%
 
U2483112.0%
 
Y2450782.0%
 
J2314061.8%
 
B2021431.6%
 
G1980641.6%
 
W1554701.2%
 
K1551331.2%
 
P1513151.2%
 
V1313631.0%
 
F998410.8%
 
Z996930.8%
 
Other values (10)1916581.5%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1122186089.3%
 
Other Punctuation11968249.5%
 
Lowercase Letter930300.7%
 
Space Separator432640.3%
 
Dash Punctuation98930.1%
 
Decimal Number345< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A127225311.3%
 
E118830210.6%
 
R9845618.8%
 
N9144408.1%
 
I7561376.7%
 
L7259956.5%
 
O7094396.3%
 
S5982145.3%
 
T5015374.5%
 
H4427813.9%
 
D4165533.7%
 
C3917493.5%
 
M3705943.3%
 
U2483112.2%
 
Y2450782.2%
 
J2314062.1%
 
B2021431.8%
 
G1980641.8%
 
W1554701.4%
 
K1551331.4%
 
P1513151.3%
 
V1313631.2%
 
F998410.9%
 
Z996930.9%
 
Q173910.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,118318698.9%
 
.104360.9%
 
'32020.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n6202066.7%
 
a3101033.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
43264100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-9893100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
234299.1%
 
430.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1131489090.0%
 
Common125032610.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A127225311.2%
 
E118830210.5%
 
R9845618.7%
 
N9144408.1%
 
I7561376.7%
 
L7259956.4%
 
O7094396.3%
 
S5982145.3%
 
T5015374.4%
 
H4427813.9%
 
D4165533.7%
 
C3917493.5%
 
M3705943.3%
 
U2483112.2%
 
Y2450782.2%
 
J2314062.0%
 
B2021431.8%
 
G1980641.8%
 
W1554701.4%
 
K1551331.4%
 
P1513151.3%
 
V1313631.2%
 
F998410.9%
 
Z996930.9%
 
n620200.5%
 
Other values (3)624980.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
,118318694.6%
 
432643.5%
 
.104360.8%
 
-98930.8%
 
'32020.3%
 
2342< 0.1%
 
43< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII12565216100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A127225310.1%
 
E11883029.5%
 
,11831869.4%
 
R9845617.8%
 
N9144407.3%
 
I7561376.0%
 
L7259955.8%
 
O7094395.6%
 
S5982144.8%
 
T5015374.0%
 
H4427813.5%
 
D4165533.3%
 
C3917493.1%
 
M3705942.9%
 
U2483112.0%
 
Y2450782.0%
 
J2314061.8%
 
B2021431.6%
 
G1980641.6%
 
W1554701.2%
 
K1551331.2%
 
P1513151.2%
 
V1313631.0%
 
F998410.8%
 
Z996930.8%
 
Other values (10)1916581.5%
 

Time1 of Occurrence
Categorical

HIGH CARDINALITY

Distinct1440
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
00:00
 
27717
18:00
 
20278
22:00
 
19799
12:00
 
18052
20:00
 
18017
Other values (1435)
646999 
ValueCountFrequency (%) 
00:00277173.7%
 
18:00202782.7%
 
22:00197992.6%
 
12:00180522.4%
 
20:00180172.4%
 
17:00178652.4%
 
19:00166832.2%
 
21:00166252.2%
 
08:00151492.0%
 
23:00137341.8%
 
16:00132571.8%
 
15:00131551.8%
 
10:00123711.6%
 
14:00111121.5%
 
13:00110441.5%
 
09:00107441.4%
 
01:00102741.4%
 
11:0097641.3%
 
02:0084921.1%
 
07:0071100.9%
 
18:3070920.9%
 
03:0070530.9%
 
17:3069190.9%
 
22:3067550.9%
 
20:3065960.9%
 
Other values (1415)42520556.6%
 
2021-03-12T16:39:39.617458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:39.767781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length5
Min length5

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0123045432.8%
 
:75086220.0%
 
152940414.1%
 
23365019.0%
 
32435306.5%
 
52043265.4%
 
41386353.7%
 
8924012.5%
 
9826582.2%
 
7779252.1%
 
6676141.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number300344880.0%
 
Other Punctuation75086220.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0123045441.0%
 
152940417.6%
 
233650111.2%
 
32435308.1%
 
52043266.8%
 
41386354.6%
 
8924013.1%
 
9826582.8%
 
7779252.6%
 
6676142.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:750862100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3754310100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0123045432.8%
 
:75086220.0%
 
152940414.1%
 
23365019.0%
 
32435306.5%
 
52043265.4%
 
41386353.7%
 
8924012.5%
 
9826582.2%
 
7779252.1%
 
6676141.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3754310100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0123045432.8%
 
:75086220.0%
 
152940414.1%
 
23365019.0%
 
32435306.5%
 
52043265.4%
 
41386353.7%
 
8924012.5%
 
9826582.2%
 
7779252.1%
 
6676141.8%
 

Responding Officer #2 Badge No
Categorical

HIGH CARDINALITY
MISSING

Distinct4289
Distinct (%)1.6%
Missing479138
Missing (%)63.8%
Memory size5.7 MiB
6700
 
854
6614
 
757
10226
 
704
8291
 
699
9480
 
673
Other values (4284)
268037 
ValueCountFrequency (%) 
67008540.1%
 
66147570.1%
 
102267040.1%
 
82916990.1%
 
94806730.1%
 
105166280.1%
 
78756210.1%
 
84246140.1%
 
106616100.1%
 
100946070.1%
 
78556060.1%
 
68326050.1%
 
80416010.1%
 
68555970.1%
 
83775910.1%
 
72705900.1%
 
87635840.1%
 
59465820.1%
 
75055660.1%
 
83155650.1%
 
106805560.1%
 
97385460.1%
 
84735450.1%
 
82255430.1%
 
92515390.1%
 
Other values (4264)25634134.1%
 
(Missing)47913863.8%
 
2021-03-12T16:39:39.960014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique449 ?
Unique (%)0.2%
2021-03-12T16:39:40.121425image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length3
Mean length3.546404
Min length3

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n95827636.0%
 
a47913818.0%
 
127229910.2%
 
01613976.1%
 
91330705.0%
 
81111674.2%
 
7993903.7%
 
6971963.7%
 
5940083.5%
 
4864993.2%
 
2849473.2%
 
3839763.2%
 
M697< 0.1%
 
F381< 0.1%
 
R279< 0.1%
 
D37< 0.1%
 
W29< 0.1%
 
21< 0.1%
 
T19< 0.1%
 
S16< 0.1%
 
I10< 0.1%
 
A2< 0.1%
 
N2< 0.1%
 
C2< 0.1%
 
B1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter143741454.0%
 
Decimal Number122394946.0%
 
Uppercase Letter14750.1%
 
Space Separator21< 0.1%
 
Dash Punctuation1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n95827666.7%
 
a47913833.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
127229922.2%
 
016139713.2%
 
913307010.9%
 
81111679.1%
 
7993908.1%
 
6971967.9%
 
5940087.7%
 
4864997.1%
 
2849476.9%
 
3839766.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M69747.3%
 
F38125.8%
 
R27918.9%
 
D372.5%
 
W292.0%
 
T191.3%
 
S161.1%
 
I100.7%
 
A20.1%
 
N20.1%
 
C20.1%
 
B10.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
21100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin143888954.0%
 
Common122397146.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n95827666.6%
 
a47913833.3%
 
M697< 0.1%
 
F381< 0.1%
 
R279< 0.1%
 
D37< 0.1%
 
W29< 0.1%
 
T19< 0.1%
 
S16< 0.1%
 
I10< 0.1%
 
A2< 0.1%
 
N2< 0.1%
 
C2< 0.1%
 
B1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
127229922.2%
 
016139713.2%
 
913307010.9%
 
81111679.1%
 
7993908.1%
 
6971967.9%
 
5940087.7%
 
4864997.1%
 
2849476.9%
 
3839766.9%
 
21< 0.1%
 
-1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2662860100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n95827636.0%
 
a47913818.0%
 
127229910.2%
 
01613976.1%
 
91330705.0%
 
81111674.2%
 
7993903.7%
 
6971963.7%
 
5940083.5%
 
4864993.2%
 
2849473.2%
 
3839763.2%
 
M697< 0.1%
 
F381< 0.1%
 
R279< 0.1%
 
D37< 0.1%
 
W29< 0.1%
 
21< 0.1%
 
T19< 0.1%
 
S16< 0.1%
 
I10< 0.1%
 
A2< 0.1%
 
N2< 0.1%
 
C2< 0.1%
 
B1< 0.1%
 

Day1 of the Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct366
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.9965586
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Memory size5.7 MiB
2021-03-12T16:39:40.286309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q198
median193
Q3277
95-th percentile348
Maximum366
Range365
Interquartile range (IQR)179

Descriptive statistics

Standard deviation105.080468
Coefficient of variation (CV)0.5589488913
Kurtosis-1.158630709
Mean187.9965586
Median Absolute Deviation (MAD)89
Skewness-0.1002785913
Sum141159472
Variance11041.90476
MonotocityNot monotonic
2021-03-12T16:39:40.440161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
130170.4%
 
18225110.3%
 
21325060.3%
 
15225030.3%
 
24424840.3%
 
20024520.3%
 
18324340.3%
 
3224300.3%
 
27424300.3%
 
21424040.3%
 
33524010.3%
 
18623840.3%
 
27823790.3%
 
18523640.3%
 
18423630.3%
 
26423480.3%
 
22623400.3%
 
24323370.3%
 
19423350.3%
 
22823340.3%
 
22123310.3%
 
19323270.3%
 
20723210.3%
 
30523210.3%
 
24823180.3%
 
Other values (341)69048892.0%
 
ValueCountFrequency (%) 
130170.4%
 
220460.3%
 
320500.3%
 
420710.3%
 
520610.3%
 
620880.3%
 
720230.3%
 
820430.3%
 
920050.3%
 
1019940.3%
 
ValueCountFrequency (%) 
3666740.1%
 
36521570.3%
 
36420560.3%
 
36320920.3%
 
36220350.3%
 
36120920.3%
 
36018030.2%
 
35917390.2%
 
35820380.3%
 
35721980.3%
 

Responding Officer #2 Name
Categorical

HIGH CARDINALITY
MISSING

Distinct4274
Distinct (%)1.6%
Missing479139
Missing (%)63.8%
Memory size5.7 MiB
FRANCIS JR,GEORGE
 
854
WILKERSON,ROBERT,C
 
757
MYTYCH,CLAYTON,ROSS
 
704
SUVANNACHAKKHAM,SOUBIN
 
699
DICKSON,JAY,CAMDAN
 
673
Other values (4269)
268036 
ValueCountFrequency (%) 
FRANCIS JR,GEORGE8540.1%
 
WILKERSON,ROBERT,C7570.1%
 
MYTYCH,CLAYTON,ROSS7040.1%
 
SUVANNACHAKKHAM,SOUBIN6990.1%
 
DICKSON,JAY,CAMDAN6730.1%
 
JOHNSON,ZACHERY6280.1%
 
BEEZLEY,JEFFREY,MICHAEL6210.1%
 
SAUERMANN,ROBERT,JOHN6140.1%
 
JARAMILLO,CARLOS6100.1%
 
PURDY,JEFFREY,THOMAS6070.1%
 
HARNER,KEITH,ALAN6060.1%
 
SANDERS,GARY,L6050.1%
 
SALDANA,IVAN,OMAR6010.1%
 
CATES,MONTE,PATRICK5970.1%
 
ELLIS,BRADLEY,C5910.1%
 
LEAL,JAIME5900.1%
 
CHAVARRIA,SERGIO,LUIS5840.1%
 
FLORES III,LUCAS5820.1%
 
CROSON,IVERY,BURNETT5660.1%
 
GEISSLER,KEITH,THOMAS5650.1%
 
ORTIZ,DIEGO5560.1%
 
CAMPBELL,DANIEL,DAVID5460.1%
 
BUI,MICHAEL,D5450.1%
 
CHRISTIAN,LAWRENCE5430.1%
 
BLALOCK,MARK,CHARLES5390.1%
 
Other values (4249)25634034.1%
 
(Missing)47913963.8%
 
2021-03-12T16:39:40.656443image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique440 ?
Unique (%)0.2%
2021-03-12T16:39:40.836046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length37
Median length3
Mean length8.227856783
Min length1

Overview of Unicode Properties

Unique unicode characters34
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n95827815.5%
 
A4864917.9%
 
a4791397.8%
 
E4527657.3%
 
,4465167.2%
 
R3746236.1%
 
N3449765.6%
 
O2816004.6%
 
I2781414.5%
 
L2749954.5%
 
S2277853.7%
 
T1863733.0%
 
H1668902.7%
 
D1565282.5%
 
C1552062.5%
 
M1484132.4%
 
Y975381.6%
 
U885841.4%
 
J869001.4%
 
B781681.3%
 
G733501.2%
 
K592861.0%
 
W591021.0%
 
P565650.9%
 
V474390.8%
 
Other values (9)1123341.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter426779969.1%
 
Lowercase Letter143741723.3%
 
Other Punctuation4523377.3%
 
Space Separator169210.3%
 
Dash Punctuation2989< 0.1%
 
Decimal Number522< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n95827866.7%
 
a47913933.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A48649111.4%
 
E45276510.6%
 
R3746238.8%
 
N3449768.1%
 
O2816006.6%
 
I2781416.5%
 
L2749956.4%
 
S2277855.3%
 
T1863734.4%
 
H1668903.9%
 
D1565283.7%
 
C1552063.6%
 
M1484133.5%
 
Y975382.3%
 
U885842.1%
 
J869002.0%
 
B781681.8%
 
G733501.7%
 
K592861.4%
 
W591021.4%
 
P565651.3%
 
V474391.1%
 
Z406051.0%
 
F348980.8%
 
X55960.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,44651698.7%
 
.57791.3%
 
'42< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
16921100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-2989100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
2522100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin570521692.3%
 
Common4727697.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n95827816.8%
 
A4864918.5%
 
a4791398.4%
 
E4527657.9%
 
R3746236.6%
 
N3449766.0%
 
O2816004.9%
 
I2781414.9%
 
L2749954.8%
 
S2277854.0%
 
T1863733.3%
 
H1668902.9%
 
D1565282.7%
 
C1552062.7%
 
M1484132.6%
 
Y975381.7%
 
U885841.6%
 
J869001.5%
 
B781681.4%
 
G733501.3%
 
K592861.0%
 
W591021.0%
 
P565651.0%
 
V474390.8%
 
Z406050.7%
 
Other values (3)454760.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
,44651694.4%
 
169213.6%
 
.57791.2%
 
-29890.6%
 
25220.1%
 
'42< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6177985100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n95827815.5%
 
A4864917.9%
 
a4791397.8%
 
E4527657.3%
 
,4465167.2%
 
R3746236.1%
 
N3449765.6%
 
O2816004.6%
 
I2781414.5%
 
L2749954.5%
 
S2277853.7%
 
T1863733.0%
 
H1668902.7%
 
D1565282.5%
 
C1552062.5%
 
M1484132.4%
 
Y975381.6%
 
U885841.4%
 
J869001.4%
 
B781681.3%
 
G733501.2%
 
K592861.0%
 
W591021.0%
 
P565650.9%
 
V474390.8%
 
Other values (9)1123341.8%
 

Reporting Officer Badge No
Categorical

HIGH CARDINALITY
MISSING

Distinct4330
Distinct (%)0.6%
Missing28126
Missing (%)3.7%
Memory size5.7 MiB
94392
 
9587
118918
 
4259
106291
 
3515
120365
 
3320
6751
 
1907
Other values (4325)
700148 
ValueCountFrequency (%) 
9439295871.3%
 
11891842590.6%
 
10629135150.5%
 
12036533200.4%
 
675119070.3%
 
316214030.2%
 
712813820.2%
 
873512230.2%
 
797012030.2%
 
970411990.2%
 
991211930.2%
 
869011420.2%
 
708011020.1%
 
740210750.1%
 
1066710670.1%
 
934710520.1%
 
1051010470.1%
 
771210440.1%
 
758710330.1%
 
758410280.1%
 
1051510110.1%
 
902610050.1%
 
96549990.1%
 
76839910.1%
 
87519680.1%
 
Other values (4305)67798190.3%
 
(Missing)281263.7%
 
2021-03-12T16:39:41.046778image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique246 ?
Unique (%)< 0.1%
2021-03-12T16:39:41.200570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length5
Mean length4.493330332
Min length3

Overview of Unicode Properties

Unique unicode characters25
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
174704722.1%
 
041357512.3%
 
934625010.3%
 
82986678.9%
 
72788538.3%
 
62703638.0%
 
52435817.2%
 
22284586.8%
 
42278886.8%
 
32278656.8%
 
n562521.7%
 
a281260.8%
 
M24640.1%
 
F1102< 0.1%
 
T915< 0.1%
 
U817< 0.1%
 
P817< 0.1%
 
R490< 0.1%
 
D282< 0.1%
 
X23< 0.1%
 
13< 0.1%
 
W10< 0.1%
 
-7< 0.1%
 
E3< 0.1%
 
N3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number328254797.3%
 
Lowercase Letter843782.5%
 
Uppercase Letter69260.2%
 
Space Separator13< 0.1%
 
Dash Punctuation7< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
174704722.8%
 
041357512.6%
 
934625010.5%
 
82986679.1%
 
72788538.5%
 
62703638.2%
 
52435817.4%
 
22284587.0%
 
42278886.9%
 
32278656.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n5625266.7%
 
a2812633.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M246435.6%
 
F110215.9%
 
T91513.2%
 
U81711.8%
 
P81711.8%
 
R4907.1%
 
D2824.1%
 
X230.3%
 
W100.1%
 
E3< 0.1%
 
N3< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-7100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
13100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common328256797.3%
 
Latin913042.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
174704722.8%
 
041357512.6%
 
934625010.5%
 
82986679.1%
 
72788538.5%
 
62703638.2%
 
52435817.4%
 
22284587.0%
 
42278886.9%
 
32278656.9%
 
13< 0.1%
 
-7< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n5625261.6%
 
a2812630.8%
 
M24642.7%
 
F11021.2%
 
T9151.0%
 
U8170.9%
 
P8170.9%
 
R4900.5%
 
D2820.3%
 
X23< 0.1%
 
W10< 0.1%
 
E3< 0.1%
 
N3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3373871100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
174704722.1%
 
041357512.3%
 
934625010.3%
 
82986678.9%
 
72788538.3%
 
62703638.0%
 
52435817.2%
 
22284586.8%
 
42278886.8%
 
32278656.8%
 
n562521.7%
 
a281260.8%
 
M24640.1%
 
F1102< 0.1%
 
T915< 0.1%
 
U817< 0.1%
 
P817< 0.1%
 
R490< 0.1%
 
D282< 0.1%
 
X23< 0.1%
 
13< 0.1%
 
W10< 0.1%
 
-7< 0.1%
 
E3< 0.1%
 
N3< 0.1%
 

Year2 of Occurrence
Real number (ℝ≥0)

HIGH CORRELATION

Distinct23
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2017.544435
Minimum1984
Maximum2021
Zeros0
Zeros (%)0.0%
Memory size5.7 MiB
2021-03-12T16:39:41.332231image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1984
5-th percentile2014
Q12016
median2018
Q32019
95-th percentile2020
Maximum2021
Range37
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.984430749
Coefficient of variation (CV)0.0009835871343
Kurtosis-0.805665881
Mean2017.544435
Median Absolute Deviation (MAD)2
Skewness-0.2274696845
Sum1514895432
Variance3.937965398
MonotocityNot monotonic
2021-03-12T16:39:41.453279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%) 
202013374317.8%
 
201912999717.3%
 
201812028516.0%
 
201610010613.3%
 
20179510612.7%
 
20159332212.4%
 
2014567557.6%
 
2021213562.8%
 
2013103< 0.1%
 
201227< 0.1%
 
201116< 0.1%
 
200913< 0.1%
 
200811< 0.1%
 
20107< 0.1%
 
20075< 0.1%
 
20052< 0.1%
 
19881< 0.1%
 
20061< 0.1%
 
19891< 0.1%
 
19921< 0.1%
 
20031< 0.1%
 
20041< 0.1%
 
19841< 0.1%
 
(Missing)1< 0.1%
 
ValueCountFrequency (%) 
19841< 0.1%
 
19881< 0.1%
 
19891< 0.1%
 
19921< 0.1%
 
20031< 0.1%
 
20041< 0.1%
 
20052< 0.1%
 
20061< 0.1%
 
20075< 0.1%
 
200811< 0.1%
 
ValueCountFrequency (%) 
2021213562.8%
 
202013374317.8%
 
201912999717.3%
 
201812028516.0%
 
20179510612.7%
 
201610010613.3%
 
20159332212.4%
 
2014567557.6%
 
2013103< 0.1%
 
201227< 0.1%
 

Assisting Officer Badge No
Categorical

HIGH CARDINALITY
MISSING

Distinct2648
Distinct (%)0.5%
Missing194218
Missing (%)25.9%
Memory size5.7 MiB
T168
 
14851
T270
 
11263
5799
 
11254
T187
 
8222
T259
 
7531
Other values (2643)
503523 
ValueCountFrequency (%) 
T168148512.0%
 
T270112631.5%
 
5799112541.5%
 
T18782221.1%
 
T25975311.0%
 
T12965810.9%
 
T16164680.9%
 
T26962110.8%
 
T27660640.8%
 
821959570.8%
 
858858100.8%
 
809757600.8%
 
661557280.8%
 
552954660.7%
 
814453180.7%
 
704649750.7%
 
810747680.6%
 
599747600.6%
 
724647000.6%
 
756946920.6%
 
671545940.6%
 
702845510.6%
 
762144160.6%
 
684843780.6%
 
667943770.6%
 
Other values (2623)39794953.0%
 
(Missing)19421825.9%
 
2021-03-12T16:39:41.653338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique601 ?
Unique (%)0.1%
2021-03-12T16:39:41.800641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length4
Mean length3.807217571
Min length3

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n38843613.6%
 
731166210.9%
 
82827419.9%
 
62643019.2%
 
12536228.9%
 
92517538.8%
 
52180737.6%
 
a1942186.8%
 
21884886.6%
 
01683045.9%
 
41422845.0%
 
31125163.9%
 
T777842.7%
 
D27170.1%
 
F937< 0.1%
 
M812< 0.1%
 
R47< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number219374476.7%
 
Lowercase Letter58265420.4%
 
Uppercase Letter822972.9%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
731166214.2%
 
828274112.9%
 
626430112.0%
 
125362211.6%
 
925175311.5%
 
52180739.9%
 
21884888.6%
 
01683047.7%
 
41422846.5%
 
31125165.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n38843666.7%
 
a19421833.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T7778494.5%
 
D27173.3%
 
F9371.1%
 
M8121.0%
 
R470.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common219374476.7%
 
Latin66495123.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
731166214.2%
 
828274112.9%
 
626430112.0%
 
125362211.6%
 
925175311.5%
 
52180739.9%
 
21884888.6%
 
01683047.7%
 
41422846.5%
 
31125165.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n38843658.4%
 
a19421829.2%
 
T7778411.7%
 
D27170.4%
 
F9370.1%
 
M8120.1%
 
R47< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2858695100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n38843613.6%
 
731166210.9%
 
82827419.9%
 
62643019.2%
 
12536228.9%
 
92517538.8%
 
52180737.6%
 
a1942186.8%
 
21884886.6%
 
01683045.9%
 
41422845.0%
 
31125163.9%
 
T777842.7%
 
D27170.1%
 
F937< 0.1%
 
M812< 0.1%
 
R47< 0.1%
 

Month2 of Occurence
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size5.7 MiB
July
70861 
August
69972 
October
67207 
September
66063 
December
65293 
Other values (7)
411465 
ValueCountFrequency (%) 
July708619.4%
 
August699729.3%
 
October672079.0%
 
September660638.8%
 
December652938.7%
 
January649018.6%
 
June642798.6%
 
November630508.4%
 
May564447.5%
 
February563707.5%
 
March541437.2%
 
April522787.0%
 
(Missing)1< 0.1%
 
2021-03-12T16:39:41.988010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:42.140107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length7
Mean length6.204680754
Min length3

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e70802415.2%
 
r54567511.7%
 
u3963558.5%
 
b3179836.8%
 
a2967606.4%
 
y2485765.3%
 
t2032424.4%
 
J2000414.3%
 
m1944064.2%
 
c1866434.0%
 
o1302572.8%
 
n1291822.8%
 
l1231392.6%
 
A1222502.6%
 
p1183412.5%
 
M1105872.4%
 
g699721.5%
 
s699721.5%
 
O672071.4%
 
S660631.4%
 
D652931.4%
 
N630501.4%
 
v630501.4%
 
F563701.2%
 
h541431.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter390799883.9%
 
Uppercase Letter75086116.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J20004126.6%
 
A12225016.3%
 
M11058714.7%
 
O672079.0%
 
S660638.8%
 
D652938.7%
 
N630508.4%
 
F563707.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e70802418.1%
 
r54567514.0%
 
u39635510.1%
 
b3179838.1%
 
a2967607.6%
 
y2485766.4%
 
t2032425.2%
 
m1944065.0%
 
c1866434.8%
 
o1302573.3%
 
n1291823.3%
 
l1231393.2%
 
p1183413.0%
 
g699721.8%
 
s699721.8%
 
v630501.6%
 
h541431.4%
 
i522781.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4658859100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e70802415.2%
 
r54567511.7%
 
u3963558.5%
 
b3179836.8%
 
a2967606.4%
 
y2485765.3%
 
t2032424.4%
 
J2000414.3%
 
m1944064.2%
 
c1866434.0%
 
o1302572.8%
 
n1291822.8%
 
l1231392.6%
 
A1222502.6%
 
p1183412.5%
 
M1105872.4%
 
g699721.5%
 
s699721.5%
 
O672071.4%
 
S660631.4%
 
D652931.4%
 
N630501.4%
 
v630501.4%
 
F563701.2%
 
h541431.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4658859100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e70802415.2%
 
r54567511.7%
 
u3963558.5%
 
b3179836.8%
 
a2967606.4%
 
y2485765.3%
 
t2032424.4%
 
J2000414.3%
 
m1944064.2%
 
c1866434.0%
 
o1302572.8%
 
n1291822.8%
 
l1231392.6%
 
A1222502.6%
 
p1183412.5%
 
M1105872.4%
 
g699721.5%
 
s699721.5%
 
O672071.4%
 
S660631.4%
 
D652931.4%
 
N630501.4%
 
v630501.4%
 
F563701.2%
 
h541431.2%
 

Reviewing Officer Badge No
Categorical

HIGH CARDINALITY

Distinct180
Distinct (%)< 0.1%
Missing2775
Missing (%)0.4%
Memory size5.7 MiB
81075
59417 
15356
 
44527
111210
 
36425
057074
 
34167
70495
 
33013
Other values (175)
540538 
ValueCountFrequency (%) 
81075594177.9%
 
15356445275.9%
 
111210364254.9%
 
057074341674.6%
 
70495330134.4%
 
118918298714.0%
 
106845277843.7%
 
36201267333.6%
 
105273263673.5%
 
54292251663.4%
 
3366235143.1%
 
120430229743.1%
 
77397221563.0%
 
105995207762.8%
 
83070203132.7%
 
106291194902.6%
 
111047187002.5%
 
113327179022.4%
 
122184158582.1%
 
118185153232.0%
 
13914150962.0%
 
121171121261.6%
 
120627119251.6%
 
97133116841.6%
 
47635115471.5%
 
Other values (155)14523319.3%
 
2021-03-12T16:39:42.313030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique26 ?
Unique (%)< 0.1%
2021-03-12T16:39:42.486007image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length5
Mean length5.431625252
Min length3

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
193768323.0%
 
051676612.7%
 
744100410.8%
 
542455910.4%
 
33873219.5%
 
23223407.9%
 
92714396.7%
 
42558016.3%
 
82546416.2%
 
62497236.1%
 
M72930.2%
 
n55500.1%
 
a27750.1%
 
-1488< 0.1%
 
N17< 0.1%
 
O1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number406127799.6%
 
Lowercase Letter83250.2%
 
Uppercase Letter73110.2%
 
Dash Punctuation1488< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
193768323.1%
 
051676612.7%
 
744100410.9%
 
542455910.5%
 
33873219.5%
 
23223407.9%
 
92714396.7%
 
42558016.3%
 
82546416.3%
 
62497236.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n555066.7%
 
a277533.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M729399.8%
 
N170.2%
 
O1< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1488100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common406276599.6%
 
Latin156360.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
193768323.1%
 
051676612.7%
 
744100410.9%
 
542455910.5%
 
33873219.5%
 
23223407.9%
 
92714396.7%
 
42558016.3%
 
82546416.3%
 
62497236.1%
 
-1488< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
M729346.6%
 
n555035.5%
 
a277517.7%
 
N170.1%
 
O1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4078401100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
193768323.0%
 
051676612.7%
 
744100410.8%
 
542455910.4%
 
33873219.5%
 
23223407.9%
 
92714396.7%
 
42558016.3%
 
82546416.2%
 
62497236.1%
 
M72930.2%
 
n55500.1%
 
a27750.1%
 
-1488< 0.1%
 
N17< 0.1%
 
O1< 0.1%
 

Day2 of the Week
Categorical

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size5.7 MiB
Mon
113001 
Fri
109065 
Sun
107477 
Sat
106990 
Thu
105414 
Other values (2)
208914 
ValueCountFrequency (%) 
Mon11300115.0%
 
Fri10906514.5%
 
Sun10747714.3%
 
Sat10699014.2%
 
Thu10541414.0%
 
Tue10483714.0%
 
Wed10407713.9%
 
(Missing)1< 0.1%
 
2021-03-12T16:39:42.647987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:42.749607image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:39:42.940375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
u31772814.1%
 
n2204809.8%
 
S2144679.5%
 
T2102519.3%
 
e2089149.3%
 
M1130015.0%
 
o1130015.0%
 
F1090654.8%
 
r1090654.8%
 
i1090654.8%
 
a1069914.7%
 
t1069904.7%
 
h1054144.7%
 
W1040774.6%
 
d1040774.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter150172566.7%
 
Uppercase Letter75086133.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S21446728.6%
 
T21025128.0%
 
M11300115.0%
 
F10906514.5%
 
W10407713.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
u31772821.2%
 
n22048014.7%
 
e20891413.9%
 
o1130017.5%
 
r1090657.3%
 
i1090657.3%
 
a1069917.1%
 
t1069907.1%
 
h1054147.0%
 
d1040776.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2252586100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
u31772814.1%
 
n2204809.8%
 
S2144679.5%
 
T2102519.3%
 
e2089149.3%
 
M1130015.0%
 
o1130015.0%
 
F1090654.8%
 
r1090654.8%
 
i1090654.8%
 
a1069914.7%
 
t1069904.7%
 
h1054144.7%
 
W1040774.6%
 
d1040774.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2252586100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
u31772814.1%
 
n2204809.8%
 
S2144679.5%
 
T2102519.3%
 
e2089149.3%
 
M1130015.0%
 
o1130015.0%
 
F1090654.8%
 
r1090654.8%
 
i1090654.8%
 
a1069914.7%
 
t1069904.7%
 
h1054144.7%
 
W1040774.6%
 
d1040774.6%
 

Element Number Assigned
Categorical

HIGH CARDINALITY
MISSING

Distinct4221
Distinct (%)0.6%
Missing29185
Missing (%)3.9%
Memory size5.7 MiB
EX07
 
9409
OFFDTY
 
8723
EX01
 
6890
EX06
 
5733
EX04
 
4502
Other values (4216)
686420 
ValueCountFrequency (%) 
EX0794091.3%
 
OFFDTY87231.2%
 
EX0168900.9%
 
EX0657330.8%
 
EX0445020.6%
 
C20924600.3%
 
C50921790.3%
 
EX0920480.3%
 
EX0519530.3%
 
EX1018770.2%
 
C30916770.2%
 
B52416630.2%
 
B50916270.2%
 
C70916020.2%
 
EX0815210.2%
 
C64412900.2%
 
B53412500.2%
 
B52212470.2%
 
B45212140.2%
 
B64411900.2%
 
B52611620.2%
 
C25411340.2%
 
B55311330.2%
 
C61111250.1%
 
B65211160.1%
 
Other values (4196)65595287.4%
 
(Missing)291853.9%
 
2021-03-12T16:39:43.104730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique571 ?
Unique (%)0.1%
2021-03-12T16:39:43.267234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length4
Mean length3.992697726
Min length2

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
234655311.6%
 
333687111.2%
 
133215111.1%
 
430686110.2%
 
52802889.3%
 
B1667375.6%
 
61655045.5%
 
71628145.4%
 
C1547765.2%
 
A1077493.6%
 
E1016513.4%
 
D857932.9%
 
F653262.2%
 
0651282.2%
 
9628222.1%
 
n583701.9%
 
8404401.3%
 
X353381.2%
 
a291851.0%
 
L193690.6%
 
T163110.5%
 
Y118320.4%
 
S90500.3%
 
O90290.3%
 
G87680.3%
 
Other values (11)192490.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number209943270.0%
 
Uppercase Letter80860227.0%
 
Lowercase Letter875552.9%
 
Space Separator23760.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B16673720.6%
 
C15477619.1%
 
A10774913.3%
 
E10165112.6%
 
D8579310.6%
 
F653268.1%
 
X353384.4%
 
L193692.4%
 
T163112.0%
 
Y118321.5%
 
S90501.1%
 
O90291.1%
 
G87681.1%
 
U38920.5%
 
P37270.5%
 
R35920.4%
 
J22850.3%
 
H21720.3%
 
W8400.1%
 
K319< 0.1%
 
M29< 0.1%
 
Z13< 0.1%
 
I4< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
234655316.5%
 
333687116.0%
 
133215115.8%
 
430686114.6%
 
528028813.4%
 
61655047.9%
 
71628147.8%
 
0651283.1%
 
9628223.0%
 
8404401.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n5837066.7%
 
a2918533.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2376100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common210180870.1%
 
Latin89615729.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
B16673718.6%
 
C15477617.3%
 
A10774912.0%
 
E10165111.3%
 
D857939.6%
 
F653267.3%
 
n583706.5%
 
X353383.9%
 
a291853.3%
 
L193692.2%
 
T163111.8%
 
Y118321.3%
 
S90501.0%
 
O90291.0%
 
G87681.0%
 
U38920.4%
 
P37270.4%
 
R35920.4%
 
J22850.3%
 
H21720.2%
 
W8400.1%
 
K319< 0.1%
 
M29< 0.1%
 
Z13< 0.1%
 
I4< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
234655316.5%
 
333687116.0%
 
133215115.8%
 
430686114.6%
 
528028813.3%
 
61655047.9%
 
71628147.7%
 
0651283.1%
 
9628223.0%
 
8404401.9%
 
23760.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2997965100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
234655311.6%
 
333687111.2%
 
133215111.1%
 
430686110.2%
 
52802889.3%
 
B1667375.6%
 
61655045.5%
 
71628145.4%
 
C1547765.2%
 
A1077493.6%
 
E1016513.4%
 
D857932.9%
 
F653262.2%
 
0651282.2%
 
9628222.1%
 
n583701.9%
 
8404401.3%
 
X353381.2%
 
a291851.0%
 
L193690.6%
 
T163110.5%
 
Y118320.4%
 
S90500.3%
 
O90290.3%
 
G87680.3%
 
Other values (11)192490.6%
 

Time2 of Occurrence
Categorical

HIGH CARDINALITY

Distinct1440
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size5.7 MiB
08:00
 
19102
07:00
 
15387
00:00
 
14739
09:00
 
14379
10:00
 
13566
Other values (1435)
673688 
ValueCountFrequency (%) 
08:00191022.5%
 
07:00153872.0%
 
00:00147392.0%
 
09:00143791.9%
 
10:00135661.8%
 
12:00128431.7%
 
06:00101931.4%
 
11:00101001.3%
 
17:0099951.3%
 
15:0093171.2%
 
16:0092541.2%
 
13:0090561.2%
 
18:0086231.1%
 
14:0085271.1%
 
07:3070260.9%
 
19:0069240.9%
 
20:0066490.9%
 
08:3064370.9%
 
05:0060210.8%
 
22:0058390.8%
 
21:0057940.8%
 
10:3055570.7%
 
09:3055160.7%
 
06:3052610.7%
 
16:3052480.7%
 
Other values (1415)51950869.2%
 
2021-03-12T16:39:43.481497image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:43.650895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length4.999997336
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0108513628.9%
 
:75086120.0%
 
155191614.7%
 
23034978.1%
 
52568386.8%
 
32565076.8%
 
41661074.4%
 
81022392.7%
 
7987532.6%
 
9927532.5%
 
6896982.4%
 
n2< 0.1%
 
a1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number300344480.0%
 
Other Punctuation75086120.0%
 
Lowercase Letter3< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0108513636.1%
 
155191618.4%
 
230349710.1%
 
52568388.6%
 
32565078.5%
 
41661075.5%
 
81022393.4%
 
7987533.3%
 
9927533.1%
 
6896983.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:750861100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n266.7%
 
a133.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3754305> 99.9%
 
Latin3< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0108513628.9%
 
:75086120.0%
 
155191614.7%
 
23034978.1%
 
52568386.8%
 
32565076.8%
 
41661074.4%
 
81022392.7%
 
7987532.6%
 
9927532.5%
 
6896982.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n266.7%
 
a133.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3754308100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0108513628.9%
 
:75086120.0%
 
155191614.7%
 
23034978.1%
 
52568386.8%
 
32565076.8%
 
41661074.4%
 
81022392.7%
 
7987532.6%
 
9927532.5%
 
6896982.4%
 
n2< 0.1%
 
a1< 0.1%
 

Investigating Unit 1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing206592
Missing (%)27.5%
Memory size5.7 MiB
Investigations
491120 
Strategic Deployment
 
40615
Support
 
8142
Patrol
 
4392
Support Personnel
 
1
ValueCountFrequency (%) 
Investigations49112065.4%
 
Strategic Deployment406155.4%
 
Support81421.1%
 
Patrol43920.6%
 
Support Personnel1< 0.1%
 
(Missing)20659227.5%
 
2021-03-12T16:39:43.820405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-03-12T16:39:43.905101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:39:44.083268image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length14
Mean length11.17531451
Min length3

Overview of Unicode Properties

Unique unicode characters21
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n143604117.1%
 
t111662013.3%
 
i102285512.2%
 
s98224111.7%
 
a7427198.9%
 
e6129677.3%
 
o5442716.5%
 
g5317356.3%
 
I4911205.9%
 
v4911205.9%
 
p569010.7%
 
r531510.6%
 
S487580.6%
 
l450080.5%
 
406160.5%
 
c406150.5%
 
D406150.5%
 
y406150.5%
 
m406150.5%
 
u81430.1%
 
P43930.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter776561792.5%
 
Uppercase Letter5848867.0%
 
Space Separator406160.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I49112084.0%
 
S487588.3%
 
D406156.9%
 
P43930.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n143604118.5%
 
t111662014.4%
 
i102285513.2%
 
s98224112.6%
 
a7427199.6%
 
e6129677.9%
 
o5442717.0%
 
g5317356.8%
 
v4911206.3%
 
p569010.7%
 
r531510.7%
 
l450080.6%
 
c406150.5%
 
y406150.5%
 
m406150.5%
 
u81430.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
40616100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin835050399.5%
 
Common406160.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n143604117.2%
 
t111662013.4%
 
i102285512.2%
 
s98224111.8%
 
a7427198.9%
 
e6129677.3%
 
o5442716.5%
 
g5317356.4%
 
I4911205.9%
 
v4911205.9%
 
p569010.7%
 
r531510.6%
 
S487580.6%
 
l450080.5%
 
c406150.5%
 
D406150.5%
 
y406150.5%
 
m406150.5%
 
u81430.1%
 
P43930.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
40616100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII8391119100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n143604117.1%
 
t111662013.3%
 
i102285512.2%
 
s98224111.7%
 
a7427198.9%
 
e6129677.3%
 
o5442716.5%
 
g5317356.3%
 
I4911205.9%
 
v4911205.9%
 
p569010.7%
 
r531510.6%
 
S487580.6%
 
l450080.5%
 
406160.5%
 
c406150.5%
 
D406150.5%
 
y406150.5%
 
m406150.5%
 
u81430.1%
 
P43930.1%
 

Day2 of the Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct366
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean187.9348734
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Memory size5.7 MiB
2021-03-12T16:39:44.254347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q198
median194
Q3277
95-th percentile348
Maximum366
Range365
Interquartile range (IQR)179

Descriptive statistics

Standard deviation105.0978254
Coefficient of variation (CV)0.5592247118
Kurtosis-1.160122099
Mean187.9348734
Median Absolute Deviation (MAD)89
Skewness-0.1004956862
Sum141112967
Variance11045.55291
MonotocityNot monotonic
2021-03-12T16:39:45.078486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
124140.3%
 
22624010.3%
 
20023920.3%
 
20223850.3%
 
18623820.3%
 
21423750.3%
 
18323680.3%
 
18223670.3%
 
15223580.3%
 
19423500.3%
 
27823490.3%
 
20823470.3%
 
23123400.3%
 
24423390.3%
 
19623330.3%
 
22823320.3%
 
25123280.3%
 
22323240.3%
 
26423220.3%
 
24323200.3%
 
27923180.3%
 
19323180.3%
 
19523170.3%
 
22123030.3%
 
18723010.3%
 
Other values (341)69217892.2%
 
ValueCountFrequency (%) 
124140.3%
 
221130.3%
 
321690.3%
 
421670.3%
 
521350.3%
 
621090.3%
 
721110.3%
 
821030.3%
 
920400.3%
 
1019140.3%
 
ValueCountFrequency (%) 
3666110.1%
 
36520750.3%
 
36421550.3%
 
36321280.3%
 
36221270.3%
 
36120830.3%
 
36017900.2%
 
35917230.2%
 
35819060.3%
 
35722120.3%
 

Investigating Unit 2
Categorical

HIGH CARDINALITY
MISSING

Distinct62
Distinct (%)< 0.1%
Missing206581
Missing (%)27.5%
Memory size5.7 MiB
Special Investigations / Auto Theft
93915 
Property Crime Division / NE Property Crimes
61188 
Property Crime Division / NW Property Crimes
49287 
Property Crime Division / SW Property Crimes
45753 
Capers / Assaults
40211 
Other values (57)
253927 
ValueCountFrequency (%) 
Special Investigations / Auto Theft9391512.5%
 
Property Crime Division / NE Property Crimes611888.1%
 
Property Crime Division / NW Property Crimes492876.6%
 
Property Crime Division / SW Property Crimes457536.1%
 
Capers / Assaults402115.4%
 
Property Crime Division / NC Property Crimes399175.3%
 
Property Crime Division / SC Property Crimes387755.2%
 
Field Services / Vehicle Crimes Unit372695.0%
 
Capers / Robbery315404.2%
 
Property Crime Division / CE Property Crimes268503.6%
 
Property Crime Division / SE Property Crimes229463.1%
 
Special Investigations / Financial Crimes180462.4%
 
Capers / Homicide97091.3%
 
Support Division / Property Room78521.0%
 
Public Integrity / Public Integrity25900.3%
 
Capers / Youth Services24230.3%
 
Comp Stat Division / Gang23210.3%
 
Capers / Family Violence22360.3%
 
Property Crime Division / Major Crimes12610.2%
 
SouthWest / SouthWest Patrol11060.1%
 
Capers / Special Investigations10400.1%
 
Arson / Arson Inv9230.1%
 
Marshalls / Marshall Inv7950.1%
 
SouthEast / SouthEast Patrol6930.1%
 
South Central / South Central Patrol6570.1%
 
Other values (37)49780.7%
 
(Missing)20658127.5%
 
2021-03-12T16:39:45.263388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)< 0.1%
2021-03-12T16:39:45.405584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length44
Median length35
Mean length27.56932299
Min length3

Overview of Unicode Properties

Unique unicode characters47
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
261062412.6%
 
i208324710.1%
 
r19757949.5%
 
e18687349.0%
 
o11705705.7%
 
s11280635.4%
 
t11182375.4%
 
n10283075.0%
 
C8290634.0%
 
p8003223.9%
 
m6503463.1%
 
a6216483.0%
 
y6175093.0%
 
P5883762.8%
 
/5442722.6%
 
v4515472.2%
 
D2968031.4%
 
S2772711.3%
 
l2693701.3%
 
c2280181.1%
 
u1554620.8%
 
N1537690.7%
 
h1435270.7%
 
A1372590.7%
 
g1215120.6%
 
Other values (22)8311074.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1464486370.7%
 
Uppercase Letter290099814.0%
 
Space Separator261062412.6%
 
Other Punctuation5442722.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C82906328.6%
 
P58837620.3%
 
D29680310.2%
 
S2772719.6%
 
N1537695.3%
 
A1372594.7%
 
I1200694.1%
 
E1133223.9%
 
W979733.4%
 
T940873.2%
 
F581622.0%
 
V395771.4%
 
R395331.4%
 
U372841.3%
 
H97090.3%
 
M31840.1%
 
Y24230.1%
 
G23230.1%
 
O523< 0.1%
 
B245< 0.1%
 
L41< 0.1%
 
K2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i208324714.2%
 
r197579413.5%
 
e186873412.8%
 
o11705708.0%
 
s11280637.7%
 
t11182377.6%
 
n10283077.0%
 
p8003225.5%
 
m6503464.4%
 
a6216484.2%
 
y6175094.2%
 
v4515473.1%
 
l2693701.8%
 
c2280181.6%
 
u1554621.1%
 
h1435271.0%
 
g1215120.8%
 
f939790.6%
 
b684310.5%
 
d483270.3%
 
j1261< 0.1%
 
x625< 0.1%
 
k27< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2610624100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/544272100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1754586184.8%
 
Common315489615.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i208324711.9%
 
r197579411.3%
 
e186873410.7%
 
o11705706.7%
 
s11280636.4%
 
t11182376.4%
 
n10283075.9%
 
C8290634.7%
 
p8003224.6%
 
m6503463.7%
 
a6216483.5%
 
y6175093.5%
 
P5883763.4%
 
v4515472.6%
 
D2968031.7%
 
S2772711.6%
 
l2693701.5%
 
c2280181.3%
 
u1554620.9%
 
N1537690.9%
 
h1435270.8%
 
A1372590.8%
 
g1215120.7%
 
I1200690.7%
 
E1133220.6%
 
Other values (20)5977163.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
261062482.7%
 
/54427217.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII20700757100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
261062412.6%
 
i208324710.1%
 
r19757949.5%
 
e18687349.0%
 
o11705705.7%
 
s11280635.4%
 
t11182375.4%
 
n10283075.0%
 
C8290634.0%
 
p8003223.9%
 
m6503463.1%
 
a6216483.0%
 
y6175093.0%
 
P5883762.8%
 
/5442722.6%
 
v4515472.2%
 
D2968031.4%
 
S2772711.3%
 
l2693701.3%
 
c2280181.1%
 
u1554620.8%
 
N1537690.7%
 
h1435270.7%
 
A1372590.7%
 
g1215120.6%
 
Other values (22)8311074.0%
 

Offense Status
Categorical

MISSING

Distinct8
Distinct (%)< 0.1%
Missing9960
Missing (%)1.3%
Memory size5.7 MiB
Suspended
609594 
Clear by Arrest
85795 
Clear by Exceptional Arrest
 
21363
Closed/Cleared
 
13274
Open
 
10842
Other values (3)
 
34
ValueCountFrequency (%) 
Suspended60959481.2%
 
Clear by Arrest8579511.4%
 
Clear by Exceptional Arrest213632.8%
 
Closed/Cleared132741.8%
 
Open108421.4%
 
Returned for Correction25< 0.1%
 
Unfounded8< 0.1%
 
L1< 0.1%
 
(Missing)99601.3%
 
2021-03-12T16:39:45.547811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-03-12T16:39:45.649276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:39:45.872802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length9
Mean length10.13475712
Min length1

Overview of Unicode Properties

Unique unicode characters27
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e150561419.8%
 
d124577716.4%
 
s7300269.6%
 
n6617858.7%
 
p6417998.4%
 
u6096278.0%
 
S6095948.0%
 
r3348484.4%
 
2357293.1%
 
l1550692.0%
 
a1517552.0%
 
C1337311.8%
 
t1285711.7%
 
b1071581.4%
 
y1071581.4%
 
A1071581.4%
 
o347200.5%
 
c213880.3%
 
i213880.3%
 
E213630.3%
 
x213630.3%
 
/132740.2%
 
O108420.1%
 
f33< 0.1%
 
R25< 0.1%
 
Other values (2)9< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter647807985.1%
 
Uppercase Letter88272211.6%
 
Space Separator2357293.1%
 
Other Punctuation132740.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S60959469.1%
 
C13373115.1%
 
A10715812.1%
 
E213632.4%
 
O108421.2%
 
R25< 0.1%
 
U8< 0.1%
 
L1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e150561423.2%
 
d124577719.2%
 
s73002611.3%
 
n66178510.2%
 
p6417999.9%
 
u6096279.4%
 
r3348485.2%
 
l1550692.4%
 
a1517552.3%
 
t1285712.0%
 
b1071581.7%
 
y1071581.7%
 
o347200.5%
 
c213880.3%
 
i213880.3%
 
x213630.3%
 
f33< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
235729100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/13274100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin736080196.7%
 
Common2490033.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e150561420.5%
 
d124577716.9%
 
s7300269.9%
 
n6617859.0%
 
p6417998.7%
 
u6096278.3%
 
S6095948.3%
 
r3348484.5%
 
l1550692.1%
 
a1517552.1%
 
C1337311.8%
 
t1285711.7%
 
b1071581.5%
 
y1071581.5%
 
A1071581.5%
 
o347200.5%
 
c213880.3%
 
i213880.3%
 
E213630.3%
 
x213630.3%
 
O108420.1%
 
f33< 0.1%
 
R25< 0.1%
 
U8< 0.1%
 
L1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
23572994.7%
 
/132745.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII7609804100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e150561419.8%
 
d124577716.4%
 
s7300269.6%
 
n6617858.7%
 
p6417998.4%
 
u6096278.0%
 
S6095948.0%
 
r3348484.4%
 
2357293.1%
 
l1550692.0%
 
a1517552.0%
 
C1337311.8%
 
t1285711.7%
 
b1071581.4%
 
y1071581.4%
 
A1071581.4%
 
o347200.5%
 
c213880.3%
 
i213880.3%
 
E213630.3%
 
x213630.3%
 
/132740.2%
 
O108420.1%
 
f33< 0.1%
 
R25< 0.1%
 
Other values (2)9< 0.1%
 

UCR Disposition
Categorical

MISSING

Distinct11
Distinct (%)< 0.1%
Missing9904
Missing (%)1.3%
Memory size5.7 MiB
Suspended
609681 
CBA (Over Age 17)
79721 
CBEA (Over Age 17)
 
20889
Closed
 
13225
Open
 
10782
Other values (6)
 
6660
ValueCountFrequency (%) 
Suspended60968181.2%
 
CBA (Over Age 17)7972110.6%
 
CBEA (Over Age 17)208892.8%
 
Closed132251.8%
 
Open107821.4%
 
CBA (Age 17)54710.7%
 
CBEA (Age 17)6510.1%
 
CBEA (Under Age 17)282< 0.1%
 
CBA (Under 17)242< 0.1%
 
Unfounded8< 0.1%
 
Pending6< 0.1%
 
(Missing)99041.3%
 
2021-03-12T16:39:46.013011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:46.155196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length9
Mean length9.926661624
Min length3

Overview of Unicode Properties

Unique unicode characters27
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e145153119.5%
 
d123313316.5%
 
n6408238.6%
 
s6229068.4%
 
p6204638.3%
 
u6096898.2%
 
S6096818.2%
 
3154044.2%
 
A2142702.9%
 
C1204811.6%
 
O1113921.5%
 
B1072561.4%
 
(1072561.4%
 
11072561.4%
 
71072561.4%
 
)1072561.4%
 
g1070201.4%
 
r1011341.4%
 
v1006101.3%
 
E218220.3%
 
o132330.2%
 
l132250.2%
 
a99040.1%
 
U532< 0.1%
 
f8< 0.1%
 
Other values (2)12< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter552368574.1%
 
Uppercase Letter118544015.9%
 
Space Separator3154044.2%
 
Decimal Number2145122.9%
 
Open Punctuation1072561.4%
 
Close Punctuation1072561.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S60968151.4%
 
A21427018.1%
 
C12048110.2%
 
O1113929.4%
 
B1072569.0%
 
E218221.8%
 
U532< 0.1%
 
P6< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e145153126.3%
 
d123313322.3%
 
n64082311.6%
 
s62290611.3%
 
p62046311.2%
 
u60968911.0%
 
g1070201.9%
 
r1011341.8%
 
v1006101.8%
 
o132330.2%
 
l132250.2%
 
a99040.2%
 
f8< 0.1%
 
i6< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
315404100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(107256100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
110725650.0%
 
710725650.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)107256100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin670912590.0%
 
Common74442810.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e145153121.6%
 
d123313318.4%
 
n6408239.6%
 
s6229069.3%
 
p6204639.2%
 
u6096899.1%
 
S6096819.1%
 
A2142703.2%
 
C1204811.8%
 
O1113921.7%
 
B1072561.6%
 
g1070201.6%
 
r1011341.5%
 
v1006101.5%
 
E218220.3%
 
o132330.2%
 
l132250.2%
 
a99040.1%
 
U532< 0.1%
 
f8< 0.1%
 
P6< 0.1%
 
i6< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
31540442.4%
 
(10725614.4%
 
110725614.4%
 
710725614.4%
 
)10725614.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII7453553100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e145153119.5%
 
d123313316.5%
 
n6408238.6%
 
s6229068.4%
 
p6204638.3%
 
u6096898.2%
 
S6096818.2%
 
3154044.2%
 
A2142702.9%
 
C1204811.6%
 
O1113921.5%
 
B1072561.4%
 
(1072561.4%
 
11072561.4%
 
71072561.4%
 
)1072561.4%
 
g1070201.4%
 
r1011341.4%
 
v1006101.3%
 
E218220.3%
 
o132330.2%
 
l132250.2%
 
a99040.1%
 
U532< 0.1%
 
f8< 0.1%
 
Other values (2)12< 0.1%
 

Offense Entered Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.551513
Minimum2014
Maximum2021
Zeros0
Zeros (%)0.0%
Memory size5.7 MiB
2021-03-12T16:39:46.279498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2018
Q32019
95-th percentile2020
Maximum2021
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.982893408
Coefficient of variation (CV)0.0009828217
Kurtosis-1.101124633
Mean2017.551513
Median Absolute Deviation (MAD)2
Skewness-0.2045416511
Sum1514902764
Variance3.931866266
MonotocityNot monotonic
2021-03-12T16:39:46.391798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
202013429517.9%
 
201912982217.3%
 
201812011116.0%
 
20169994413.3%
 
20179496512.6%
 
20159324012.4%
 
2014565787.5%
 
2021219072.9%
 
ValueCountFrequency (%) 
2014565787.5%
 
20159324012.4%
 
20169994413.3%
 
20179496512.6%
 
201812011116.0%
 
201912982217.3%
 
202013429517.9%
 
2021219072.9%
 
ValueCountFrequency (%) 
2021219072.9%
 
202013429517.9%
 
201912982217.3%
 
201812011116.0%
 
20179496512.6%
 
20169994413.3%
 
20159324012.4%
 
2014565787.5%
 

Family Offense
Boolean

MISSING

Distinct1
Distinct (%)< 0.1%
Missing27379
Missing (%)3.6%
Memory size5.7 MiB
False
723483 
(Missing)
 
27379
ValueCountFrequency (%) 
False72348396.4%
 
(Missing)273793.6%
 
2021-03-12T16:39:46.503264image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Offense Entered Month
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
July
70939 
August
70149 
October
67192 
September
66107 
January
65471 
Other values (7)
411004 
ValueCountFrequency (%) 
July709399.4%
 
August701499.3%
 
October671928.9%
 
September661078.8%
 
January654718.7%
 
December650138.7%
 
June643248.6%
 
November628688.4%
 
February563497.5%
 
May557227.4%
 
March545477.3%
 
April521816.9%
 
2021-03-12T16:39:46.604808image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:46.757154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length7
Mean length6.206461374
Min length3

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e70696115.2%
 
r54607711.7%
 
u3973818.5%
 
b3175296.8%
 
a2975606.4%
 
y2484815.3%
 
t2034484.4%
 
J2007344.3%
 
m1939884.2%
 
c1867524.0%
 
o1300602.8%
 
n1297952.8%
 
l1231202.6%
 
A1223302.6%
 
p1182882.5%
 
M1102692.4%
 
g701491.5%
 
s701491.5%
 
O671921.4%
 
S661071.4%
 
D650131.4%
 
N628681.3%
 
v628681.3%
 
F563491.2%
 
h545471.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter390933483.9%
 
Uppercase Letter75086216.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J20073426.7%
 
A12233016.3%
 
M11026914.7%
 
O671928.9%
 
S661078.8%
 
D650138.7%
 
N628688.4%
 
F563497.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e70696118.1%
 
r54607714.0%
 
u39738110.2%
 
b3175298.1%
 
a2975607.6%
 
y2484816.4%
 
t2034485.2%
 
m1939885.0%
 
c1867524.8%
 
o1300603.3%
 
n1297953.3%
 
l1231203.1%
 
p1182883.0%
 
g701491.8%
 
s701491.8%
 
v628681.6%
 
h545471.4%
 
i521811.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4660196100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e70696115.2%
 
r54607711.7%
 
u3973818.5%
 
b3175296.8%
 
a2975606.4%
 
y2484815.3%
 
t2034484.4%
 
J2007344.3%
 
m1939884.2%
 
c1867524.0%
 
o1300602.8%
 
n1297952.8%
 
l1231202.6%
 
A1223302.6%
 
p1182882.5%
 
M1102692.4%
 
g701491.5%
 
s701491.5%
 
O671921.4%
 
S661071.4%
 
D650131.4%
 
N628681.3%
 
v628681.3%
 
F563491.2%
 
h545471.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4660196100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e70696115.2%
 
r54607711.7%
 
u3973818.5%
 
b3175296.8%
 
a2975606.4%
 
y2484815.3%
 
t2034484.4%
 
J2007344.3%
 
m1939884.2%
 
c1867524.0%
 
o1300602.8%
 
n1297952.8%
 
l1231202.6%
 
A1223302.6%
 
p1182882.5%
 
M1102692.4%
 
g701491.5%
 
s701491.5%
 
O671921.4%
 
S661071.4%
 
D650131.4%
 
N628681.3%
 
v628681.3%
 
F563491.2%
 
h545471.2%
 

Hate Crime
Boolean

MISSING

Distinct2
Distinct (%)0.2%
Missing750045
Missing (%)99.9%
Memory size5.7 MiB
No
 
504
Yes
 
313
(Missing)
750045 
ValueCountFrequency (%) 
No5040.1%
 
Yes313< 0.1%
 
(Missing)75004599.9%
 
2021-03-12T16:39:46.858695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Offense Entered Day of the Week
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
Mon
118157 
Fri
108966 
Tue
108685 
Thu
107565 
Wed
107310 
Other values (2)
200179 
ValueCountFrequency (%) 
Mon11815715.7%
 
Fri10896614.5%
 
Tue10868514.5%
 
Thu10756514.3%
 
Wed10731014.3%
 
Sat10062513.4%
 
Sun9955413.3%
 
2021-03-12T16:39:46.970328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:47.081978image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:39:47.273078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
u31580414.0%
 
n2177119.7%
 
T2162509.6%
 
e2159959.6%
 
S2001798.9%
 
M1181575.2%
 
o1181575.2%
 
F1089664.8%
 
r1089664.8%
 
i1089664.8%
 
h1075654.8%
 
W1073104.8%
 
d1073104.8%
 
a1006254.5%
 
t1006254.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter150172466.7%
 
Uppercase Letter75086233.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T21625028.8%
 
S20017926.7%
 
M11815715.7%
 
F10896614.5%
 
W10731014.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
u31580421.0%
 
n21771114.5%
 
e21599514.4%
 
o1181577.9%
 
r1089667.3%
 
i1089667.3%
 
h1075657.2%
 
d1073107.1%
 
a1006256.7%
 
t1006256.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2252586100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
u31580414.0%
 
n2177119.7%
 
T2162509.6%
 
e2159959.6%
 
S2001798.9%
 
M1181575.2%
 
o1181575.2%
 
F1089664.8%
 
r1089664.8%
 
i1089664.8%
 
h1075654.8%
 
W1073104.8%
 
d1073104.8%
 
a1006254.5%
 
t1006254.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2252586100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
u31580414.0%
 
n2177119.7%
 
T2162509.6%
 
e2159959.6%
 
S2001798.9%
 
M1181575.2%
 
o1181575.2%
 
F1089664.8%
 
r1089664.8%
 
i1089664.8%
 
h1075654.8%
 
W1073104.8%
 
d1073104.8%
 
a1006254.5%
 
t1006254.5%
 

Hate Crime Description
Categorical

Distinct26
Distinct (%)< 0.1%
Missing1226
Missing (%)0.2%
Memory size5.7 MiB
None
725766 
Unknown
 
23396
Anti White
 
98
Anti Black Or African American
 
78
Anti Homosexual (Gays and Lesbians)
 
67
Other values (21)
 
231
ValueCountFrequency (%) 
None72576696.7%
 
Unknown233963.1%
 
Anti White98< 0.1%
 
Anti Black Or African American78< 0.1%
 
Anti Homosexual (Gays and Lesbians)67< 0.1%
 
Anti Hispanic41< 0.1%
 
Anti Other Ethnicity/Natl Origin31< 0.1%
 
Anti Male Homosexual (Gay)29< 0.1%
 
Anti Jewish24< 0.1%
 
Anti Female Homosexual (Lesbian)16< 0.1%
 
Anti Multi-Racial Group13< 0.1%
 
Anti Other Religion10< 0.1%
 
Anti Arab9< 0.1%
 
Anti Islamic (Muslim)9< 0.1%
 
Anti Protestant8< 0.1%
 
Anti Am. Indian/Alaskan Native7< 0.1%
 
Anti Asian/Pacific Islander7< 0.1%
 
Anti-Hawaiian Or Other Pacific Islander7< 0.1%
 
Anti Physical Disability5< 0.1%
 
Anti Sikh4< 0.1%
 
Anti Transgender4< 0.1%
 
Anti Multi-Religious Group3< 0.1%
 
Anti Athieism/Agnostic1< 0.1%
 
Anti Catholic1< 0.1%
 
Anti Bisexual1< 0.1%
 
(Missing)12260.2%
 
2021-03-12T16:39:47.417300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2021-03-12T16:39:47.587840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length39
Median length4
Mean length4.103473075
Min length3

Overview of Unicode Properties

Unique unicode characters49
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n79936625.9%
 
o74942724.3%
 
e72632223.6%
 
N72580423.6%
 
k234850.8%
 
w234270.8%
 
U233960.8%
 
a20870.1%
 
i1206< 0.1%
 
1143< 0.1%
 
t768< 0.1%
 
A662< 0.1%
 
s468< 0.1%
 
r376< 0.1%
 
c363< 0.1%
 
l359< 0.1%
 
m233< 0.1%
 
h212< 0.1%
 
O164< 0.1%
 
H160< 0.1%
 
u157< 0.1%
 
y137< 0.1%
 
(121< 0.1%
 
)121< 0.1%
 
x113< 0.1%
 
Other values (24)1065< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter232890075.6%
 
Uppercase Letter75078124.4%
 
Space Separator1143< 0.1%
 
Open Punctuation121< 0.1%
 
Close Punctuation121< 0.1%
 
Other Punctuation53< 0.1%
 
Dash Punctuation23< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N72580496.7%
 
U233963.1%
 
A6620.1%
 
O164< 0.1%
 
H160< 0.1%
 
G112< 0.1%
 
W98< 0.1%
 
L83< 0.1%
 
B79< 0.1%
 
M55< 0.1%
 
E31< 0.1%
 
I30< 0.1%
 
P27< 0.1%
 
R26< 0.1%
 
J24< 0.1%
 
F16< 0.1%
 
D5< 0.1%
 
S4< 0.1%
 
T4< 0.1%
 
C1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n79936634.3%
 
o74942732.2%
 
e72632231.2%
 
k234851.0%
 
w234271.0%
 
a20870.1%
 
i12060.1%
 
t768< 0.1%
 
s468< 0.1%
 
r376< 0.1%
 
c363< 0.1%
 
l359< 0.1%
 
m233< 0.1%
 
h212< 0.1%
 
u157< 0.1%
 
y137< 0.1%
 
x113< 0.1%
 
b97< 0.1%
 
f92< 0.1%
 
d92< 0.1%
 
p57< 0.1%
 
g49< 0.1%
 
v7< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1143100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-23100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(121100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)121100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/4686.8%
 
.713.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3079681> 99.9%
 
Common1461< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n79936626.0%
 
o74942724.3%
 
e72632223.6%
 
N72580423.6%
 
k234850.8%
 
w234270.8%
 
U233960.8%
 
a20870.1%
 
i1206< 0.1%
 
t768< 0.1%
 
A662< 0.1%
 
s468< 0.1%
 
r376< 0.1%
 
c363< 0.1%
 
l359< 0.1%
 
m233< 0.1%
 
h212< 0.1%
 
O164< 0.1%
 
H160< 0.1%
 
u157< 0.1%
 
y137< 0.1%
 
x113< 0.1%
 
G112< 0.1%
 
W98< 0.1%
 
b97< 0.1%
 
Other values (18)682< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
114378.2%
 
(1218.3%
 
)1218.3%
 
/463.1%
 
-231.6%
 
.70.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3081142100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n79936625.9%
 
o74942724.3%
 
e72632223.6%
 
N72580423.6%
 
k234850.8%
 
w234270.8%
 
U233960.8%
 
a20870.1%
 
i1206< 0.1%
 
1143< 0.1%
 
t768< 0.1%
 
A662< 0.1%
 
s468< 0.1%
 
r376< 0.1%
 
c363< 0.1%
 
l359< 0.1%
 
m233< 0.1%
 
h212< 0.1%
 
O164< 0.1%
 
H160< 0.1%
 
u157< 0.1%
 
y137< 0.1%
 
(121< 0.1%
 
)121< 0.1%
 
x113< 0.1%
 
Other values (24)1065< 0.1%
 

Offense Entered Time
Categorical

HIGH CARDINALITY

Distinct1440
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
17:00
 
1061
16:55
 
1052
17:10
 
1025
16:50
 
1004
17:05
 
976
Other values (1435)
745744 
ValueCountFrequency (%) 
17:0010610.1%
 
16:5510520.1%
 
17:1010250.1%
 
16:5010040.1%
 
17:059760.1%
 
16:459650.1%
 
16:409650.1%
 
16:589630.1%
 
16:599610.1%
 
17:019580.1%
 
09:559470.1%
 
16:549400.1%
 
16:519340.1%
 
10:009300.1%
 
16:499280.1%
 
17:029230.1%
 
10:059170.1%
 
09:009090.1%
 
16:569080.1%
 
10:109060.1%
 
16:529020.1%
 
09:458980.1%
 
17:208920.1%
 
17:258910.1%
 
17:158900.1%
 
Other values (1415)72721796.9%
 
2021-03-12T16:39:47.762487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:47.930381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length5
Min length5

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
:75086220.0%
 
169003218.4%
 
056726815.1%
 
239431510.5%
 
32745247.3%
 
52563146.8%
 
42464956.6%
 
91528934.1%
 
81487444.0%
 
71396663.7%
 
61331973.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number300344880.0%
 
Other Punctuation75086220.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
169003223.0%
 
056726818.9%
 
239431513.1%
 
32745249.1%
 
52563148.5%
 
42464958.2%
 
91528935.1%
 
81487445.0%
 
71396664.7%
 
61331974.4%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:750862100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3754310100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
:75086220.0%
 
169003218.4%
 
056726815.1%
 
239431510.5%
 
32745247.3%
 
52563146.8%
 
42464956.6%
 
91528934.1%
 
81487444.0%
 
71396663.7%
 
61331973.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3754310100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
:75086220.0%
 
169003218.4%
 
056726815.1%
 
239431510.5%
 
32745247.3%
 
52563146.8%
 
42464956.6%
 
91528934.1%
 
81487444.0%
 
71396663.7%
 
61331973.5%
 

Weapon Used
Categorical

MISSING

Distinct36
Distinct (%)< 0.1%
Missing353483
Missing (%)47.1%
Memory size5.7 MiB
Other
238706 
None (Mutually Exclusive)
52675 
Personal Weapons (Hands-Feet ETC)
38339 
Handgun
35368 
Vehicle
 
5330
Other values (31)
26961 
ValueCountFrequency (%) 
Other23870631.8%
 
None (Mutually Exclusive)526757.0%
 
Personal Weapons (Hands-Feet ETC)383395.1%
 
Handgun353684.7%
 
Vehicle53300.7%
 
Threats42810.6%
 
Other Cutting Stabbing Inst.29700.4%
 
Firearm (Type Not Stated)27340.4%
 
Omission/Neglect27080.4%
 
Missile/Rock23340.3%
 
Pocket Knife23190.3%
 
Unknown19370.3%
 
Other Firearm15290.2%
 
Rifle11390.2%
 
Other Gun9120.1%
 
BlackJack/Club/Brick/Bat/Tire Iron7170.1%
 
Butcher Knife7000.1%
 
Shotgun6830.1%
 
Assault Weapon4550.1%
 
Blunt Object4040.1%
 
Motor Vehicle299< 0.1%
 
Simulated Gun181< 0.1%
 
Toy Gun165< 0.1%
 
Explosives150< 0.1%
 
Any Weapon of Force Deadly Disease, ETC58< 0.1%
 
Other values (11)286< 0.1%
 
(Missing)35348347.1%
 
2021-03-12T16:39:48.098596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:48.267992image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length39
Median length5
Mean length7.342455738
Min length3

Overview of Unicode Properties

Unique unicode characters52
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n96976617.6%
 
a57449610.4%
 
e5454209.9%
 
t3684606.7%
 
r2987235.4%
 
h2554354.6%
 
O2472294.5%
 
2456174.5%
 
l2110033.8%
 
u2008433.6%
 
s1865193.4%
 
o1446662.6%
 
(937481.7%
 
)937481.7%
 
E912221.7%
 
i848471.5%
 
d768071.4%
 
H737071.3%
 
c691451.3%
 
N581381.1%
 
y557051.0%
 
M553631.0%
 
x528931.0%
 
v528381.0%
 
T462940.8%
 
Other values (27)3605396.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter426379177.3%
 
Uppercase Letter76684413.9%
 
Space Separator2456174.5%
 
Open Punctuation937481.7%
 
Close Punctuation937481.7%
 
Dash Punctuation383390.7%
 
Other Punctuation110840.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n96976622.7%
 
a57449613.5%
 
e54542012.8%
 
t3684608.6%
 
r2987237.0%
 
h2554356.0%
 
l2110034.9%
 
u2008434.7%
 
s1865194.4%
 
o1446663.4%
 
i848472.0%
 
d768071.8%
 
c691451.6%
 
y557051.3%
 
x528931.2%
 
v528381.2%
 
g447661.0%
 
p417591.0%
 
k87610.2%
 
b71730.2%
 
m71520.2%
 
f42160.1%
 
w1994< 0.1%
 
j404< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O24722932.2%
 
E9122211.9%
 
H737079.6%
 
N581387.6%
 
M553637.2%
 
T462946.0%
 
F426735.6%
 
C421735.5%
 
P407455.3%
 
W388525.1%
 
S66730.9%
 
V56290.7%
 
I37200.5%
 
R34730.5%
 
B32910.4%
 
K30190.4%
 
U19370.3%
 
G13130.2%
 
J7170.1%
 
A5260.1%
 
D150< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
245617100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(93748100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-38339100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)93748100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/805672.7%
 
.297026.8%
 
,580.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin503063591.2%
 
Common4825368.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n96976619.3%
 
a57449611.4%
 
e54542010.8%
 
t3684607.3%
 
r2987235.9%
 
h2554355.1%
 
O2472294.9%
 
l2110034.2%
 
u2008434.0%
 
s1865193.7%
 
o1446662.9%
 
E912221.8%
 
i848471.7%
 
d768071.5%
 
H737071.5%
 
c691451.4%
 
N581381.2%
 
y557051.1%
 
M553631.1%
 
x528931.1%
 
v528381.1%
 
T462940.9%
 
g447660.9%
 
F426730.8%
 
C421730.8%
 
Other values (20)1815043.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
24561750.9%
 
(9374819.4%
 
)9374819.4%
 
-383397.9%
 
/80561.7%
 
.29700.6%
 
,58< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5513171100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n96976617.6%
 
a57449610.4%
 
e5454209.9%
 
t3684606.7%
 
r2987235.4%
 
h2554354.6%
 
O2472294.5%
 
2456174.5%
 
l2110033.8%
 
u2008433.6%
 
s1865193.4%
 
o1446662.6%
 
(937481.7%
 
)937481.7%
 
E912221.7%
 
i848471.5%
 
d768071.4%
 
H737071.3%
 
c691451.3%
 
N581381.1%
 
y557051.0%
 
M553631.0%
 
x528931.0%
 
v528381.0%
 
T462940.8%
 
Other values (27)3605396.5%
 

Offense Entered Date/Time
Real number (ℝ≥0)

HIGH CORRELATION

Distinct366
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.7345491
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Memory size5.7 MiB
2021-03-12T16:39:48.453071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q198
median193
Q3277
95-th percentile348
Maximum366
Range365
Interquartile range (IQR)179

Descriptive statistics

Standard deviation105.1451643
Coefficient of variation (CV)0.5600735977
Kurtosis-1.162083894
Mean187.7345491
Median Absolute Deviation (MAD)89
Skewness-0.1003326408
Sum140962739
Variance11055.50558
MonotocityNot monotonic
2021-03-12T16:39:48.615938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
23727770.4%
 
18324210.3%
 
19024040.3%
 
24823900.3%
 
22623790.3%
 
21423760.3%
 
20423670.3%
 
21823590.3%
 
18623530.3%
 
19223530.3%
 
29523480.3%
 
19423470.3%
 
19723460.3%
 
31023420.3%
 
26723420.3%
 
21023380.3%
 
21923370.3%
 
16923360.3%
 
19523340.3%
 
18223310.3%
 
20223270.3%
 
20323220.3%
 
18923190.3%
 
26123150.3%
 
19823120.3%
 
Other values (341)69168792.1%
 
ValueCountFrequency (%) 
121790.3%
 
222610.3%
 
322140.3%
 
422420.3%
 
521680.3%
 
621260.3%
 
722260.3%
 
821100.3%
 
921070.3%
 
1018800.3%
 
ValueCountFrequency (%) 
3665750.1%
 
36520270.3%
 
36421790.3%
 
36321790.3%
 
36221280.3%
 
36120530.3%
 
36017830.2%
 
35915780.2%
 
35819040.3%
 
35721760.3%
 

Gang Related Offense
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing324256
Missing (%)43.2%
Memory size5.7 MiB
No
343956 
UNK
80267 
Yes
 
1430
G
 
875
J
 
78
ValueCountFrequency (%) 
No34395645.8%
 
UNK8026710.7%
 
Yes14300.2%
 
G8750.1%
 
J78< 0.1%
 
(Missing)32425643.2%
 
2021-03-12T16:39:48.785317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:48.885737image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:39:49.058801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.539380073
Min length1

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n64851234.0%
 
N42422322.2%
 
o34395618.0%
 
a32425617.0%
 
U802674.2%
 
K802674.2%
 
Y14300.1%
 
e14300.1%
 
s14300.1%
 
G875< 0.1%
 
J78< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter131958469.2%
 
Uppercase Letter58714030.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n64851249.1%
 
o34395626.1%
 
a32425624.6%
 
e14300.1%
 
s14300.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N42422372.3%
 
U8026713.7%
 
K8026713.7%
 
Y14300.2%
 
G8750.1%
 
J78< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1906724100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n64851234.0%
 
N42422322.2%
 
o34395618.0%
 
a32425617.0%
 
U802674.2%
 
K802674.2%
 
Y14300.1%
 
e14300.1%
 
s14300.1%
 
G875< 0.1%
 
J78< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1906724100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n64851234.0%
 
N42422322.2%
 
o34395618.0%
 
a32425617.0%
 
U802674.2%
 
K802674.2%
 
Y14300.1%
 
e14300.1%
 
s14300.1%
 
G875< 0.1%
 
J78< 0.1%
 

Victim Package
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing750862
Missing (%)100.0%
Memory size5.7 MiB

CFS Number
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct644806
Distinct (%)89.2%
Missing28074
Missing (%)3.7%
Memory size5.7 MiB
17-1798291
 
139
20-0979386
 
75
19-1518879
 
60
15-2011150
 
51
21-0257406
 
50
Other values (644801)
722413 
ValueCountFrequency (%) 
17-1798291139< 0.1%
 
20-097938675< 0.1%
 
19-151887960< 0.1%
 
15-201115051< 0.1%
 
21-025740650< 0.1%
 
19-205829746< 0.1%
 
17-201237845< 0.1%
 
20-045044440< 0.1%
 
15-195896440< 0.1%
 
17-200285539< 0.1%
 
15-208762938< 0.1%
 
20-005545036< 0.1%
 
19-197672136< 0.1%
 
16-202072835< 0.1%
 
20-135125935< 0.1%
 
18-161371935< 0.1%
 
17-196341035< 0.1%
 
19-191524034< 0.1%
 
19-196957734< 0.1%
 
16-207738333< 0.1%
 
16-197087933< 0.1%
 
19-090350833< 0.1%
 
20-013842433< 0.1%
 
16-209943132< 0.1%
 
19-004154731< 0.1%
 
Other values (644781)72169096.1%
 
(Missing)280743.7%
 
2021-03-12T16:39:52.728233image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique593836 ?
Unique (%)82.2%
2021-03-12T16:39:52.898345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.738276807
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1136489418.7%
 
084131811.5%
 
273712310.1%
 
-7227889.9%
 
95356117.3%
 
85321537.3%
 
55205457.1%
 
65188017.1%
 
75103047.0%
 
44940286.8%
 
34503156.2%
 
n561480.8%
 
a280740.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number650509289.0%
 
Dash Punctuation7227889.9%
 
Lowercase Letter842221.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1136489421.0%
 
084131812.9%
 
273712311.3%
 
95356118.2%
 
85321538.2%
 
55205458.0%
 
65188018.0%
 
75103047.8%
 
44940287.6%
 
34503156.9%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-722788100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n5614866.7%
 
a2807433.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common722788098.8%
 
Latin842221.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
1136489418.9%
 
084131811.6%
 
273712310.2%
 
-72278810.0%
 
95356117.4%
 
85321537.4%
 
55205457.2%
 
65188017.2%
 
75103047.1%
 
44940286.8%
 
34503156.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n5614866.7%
 
a2807433.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII7312102100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1136489418.7%
 
084131811.5%
 
273712310.1%
 
-7227889.9%
 
95356117.3%
 
85321537.3%
 
55205457.1%
 
65188017.1%
 
75103047.0%
 
44940286.8%
 
34503156.2%
 
n561480.8%
 
a280740.4%
 

Drug Related Istevencident
Categorical

MISSING

Distinct6
Distinct (%)< 0.1%
Missing27392
Missing (%)3.6%
Memory size5.7 MiB
No
624642 
UNK
77035 
Yes
 
21778
Unknown
 
12
3
 
2
ValueCountFrequency (%) 
No62464283.2%
 
UNK7703510.3%
 
Yes217782.9%
 
Unknown12< 0.1%
 
32< 0.1%
 
21< 0.1%
 
(Missing)273923.6%
 
2021-03-12T16:39:53.042218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-03-12T16:39:53.141716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:39:53.314509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length2
Mean length2.168156066
Min length1

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N70167743.1%
 
o62465438.4%
 
U770474.7%
 
K770354.7%
 
n548203.4%
 
a273921.7%
 
Y217781.3%
 
e217781.3%
 
s217781.3%
 
k12< 0.1%
 
w12< 0.1%
 
32< 0.1%
 
21< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter87753753.9%
 
Lowercase Letter75044646.1%
 
Decimal Number3< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N70167780.0%
 
U770478.8%
 
K770358.8%
 
Y217782.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o62465483.2%
 
n548207.3%
 
a273923.7%
 
e217782.9%
 
s217782.9%
 
k12< 0.1%
 
w12< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
3266.7%
 
2133.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1627983> 99.9%
 
Common3< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N70167743.1%
 
o62465438.4%
 
U770474.7%
 
K770354.7%
 
n548203.4%
 
a273921.7%
 
Y217781.3%
 
e217781.3%
 
s217781.3%
 
k12< 0.1%
 
w12< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
3266.7%
 
2133.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1627986100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N70167743.1%
 
o62465438.4%
 
U770474.7%
 
K770354.7%
 
n548203.4%
 
a273921.7%
 
Y217781.3%
 
e217781.3%
 
s217781.3%
 
k12< 0.1%
 
w12< 0.1%
 
32< 0.1%
 
21< 0.1%
 

RMS Code
Categorical

HIGH CARDINALITY

Distinct1298
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
MA-22990004-F1
82999 
NA-99999999-X3
 
27075
F2-22990002-E5
 
25778
FS-22990001-E1
 
21694
NA-99999999-X6
 
20134
Other values (1293)
573182 
ValueCountFrequency (%) 
MA-22990004-F18299911.1%
 
NA-99999999-X3270753.6%
 
F2-22990002-E5257783.4%
 
FS-22990001-E1216942.9%
 
NA-99999999-X6201342.7%
 
FS-24110003-G1199802.7%
 
NA-99999999-MSC11189592.5%
 
MB-29990042-L99180402.4%
 
F1-12990002-C4170372.3%
 
MB-29990016-L82166772.2%
 
FS-24110003-G13162352.2%
 
MC-99999999-H29139541.9%
 
NA-99999999-X5135301.8%
 
F2-22990002-E6131931.8%
 
MC-99999999-NC112125811.7%
 
MA-13990001-H1125431.7%
 
F2-13150005-D4116781.6%
 
NA-99999999-X1106071.4%
 
MC-99999999-NC5493831.2%
 
FS-24110003-G1490291.2%
 
MB-29990016-L3783381.1%
 
MB-23990191-F18383271.1%
 
MC-99999999-L6080391.1%
 
NA-99999999-W178541.0%
 
MB-23990067-F17271300.9%
 
Other values (1273)32006842.6%
 
2021-03-12T16:39:53.476726image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique254 ?
Unique (%)< 0.1%
2021-03-12T16:39:53.641238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length15
Mean length14.82527016
Min length14

Overview of Unicode Properties

Unique unicode characters38
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
9290132626.1%
 
-150172413.5%
 
0143503312.9%
 
28681437.8%
 
18400067.5%
 
M4154303.7%
 
F4014073.6%
 
33915303.5%
 
43804953.4%
 
A3083342.8%
 
52102231.9%
 
C1921211.7%
 
N1845021.7%
 
S1612591.4%
 
61403141.3%
 
B1252301.1%
 
71068601.0%
 
8941360.8%
 
L886390.8%
 
X750930.7%
 
E668560.6%
 
G581740.5%
 
H392550.4%
 
V221590.2%
 
U193700.2%
 
Other values (13)1041130.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number736806666.2%
 
Uppercase Letter225804520.3%
 
Dash Punctuation150172413.5%
 
Other Punctuation3897< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M41543018.4%
 
F40140717.8%
 
A30833413.7%
 
C1921218.5%
 
N1845028.2%
 
S1612597.1%
 
B1252305.5%
 
L886393.9%
 
X750933.3%
 
E668563.0%
 
G581742.6%
 
H392551.7%
 
V221591.0%
 
U193700.9%
 
D163910.7%
 
Z163650.7%
 
O140950.6%
 
W138420.6%
 
K119900.5%
 
T117410.5%
 
R65270.3%
 
J42190.2%
 
Q34330.2%
 
I14480.1%
 
P152< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1501724100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
9290132639.4%
 
0143503319.5%
 
286814311.8%
 
184000611.4%
 
33915305.3%
 
43804955.2%
 
52102232.9%
 
61403141.9%
 
71068601.5%
 
8941361.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
*3897100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common887368779.7%
 
Latin225804520.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
M41543018.4%
 
F40140717.8%
 
A30833413.7%
 
C1921218.5%
 
N1845028.2%
 
S1612597.1%
 
B1252305.5%
 
L886393.9%
 
X750933.3%
 
E668563.0%
 
G581742.6%
 
H392551.7%
 
V221591.0%
 
U193700.9%
 
D163910.7%
 
Z163650.7%
 
O140950.6%
 
W138420.6%
 
K119900.5%
 
T117410.5%
 
R65270.3%
 
J42190.2%
 
Q34330.2%
 
I14480.1%
 
P152< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
9290132632.7%
 
-150172416.9%
 
0143503316.2%
 
28681439.8%
 
18400069.5%
 
33915304.4%
 
43804954.3%
 
52102232.4%
 
61403141.6%
 
71068601.2%
 
8941361.1%
 
*3897< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII11131732100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
9290132626.1%
 
-150172413.5%
 
0143503312.9%
 
28681437.8%
 
18400067.5%
 
M4154303.7%
 
F4014073.6%
 
33915303.5%
 
43804953.4%
 
A3083342.8%
 
52102231.9%
 
C1921211.7%
 
N1845021.7%
 
S1612591.4%
 
61403141.3%
 
B1252301.1%
 
71068601.0%
 
8941360.8%
 
L886390.8%
 
X750930.7%
 
E668560.6%
 
G581740.5%
 
H392550.4%
 
V221590.2%
 
U193700.2%
 
Other values (13)1041130.9%
 

Criminal Justice Information Service Code
Real number (ℝ≥0)

Distinct648
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50481039.4
Minimum9990017
Maximum99999999
Zeros0
Zeros (%)0.0%
Memory size5.7 MiB
2021-03-12T16:39:53.819172image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum9990017
5-th percentile13150005
Q122990004
median29990002
Q399999999
95-th percentile99999999
Maximum99999999
Range90009982
Interquartile range (IQR)77009995

Descriptive statistics

Standard deviation35315890.68
Coefficient of variation (CV)0.6995872333
Kurtosis-1.50004652
Mean50481039.4
Median Absolute Deviation (MAD)7000001
Skewness0.5854999645
Sum3.79042942e+13
Variance1.247212135e+15
MonotocityNot monotonic
2021-03-12T16:39:53.974348image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9999999924131532.1%
 
229900048860711.8%
 
24110003532417.1%
 
22990002398595.3%
 
22990001269093.6%
 
29990016250153.3%
 
12990002227083.0%
 
29990042180402.4%
 
23990067179002.4%
 
23990191138491.8%
 
13990001125431.7%
 
54990007122691.6%
 
13150005116931.6%
 
2399000393561.2%
 
2399019391541.2%
 
2999000282401.1%
 
1299000172421.0%
 
2999004369110.9%
 
2399000467870.9%
 
2399019464660.9%
 
5707001053020.7%
 
5499000850980.7%
 
5399000450240.7%
 
3562000849070.7%
 
2411000445530.6%
 
Other values (623)8787411.7%
 
ValueCountFrequency (%) 
999001743< 0.1%
 
9990018118< 0.1%
 
9990019253< 0.1%
 
9990022124< 0.1%
 
99900231< 0.1%
 
999002637< 0.1%
 
9990030236< 0.1%
 
1099000137< 0.1%
 
109900031< 0.1%
 
1099000450< 0.1%
 
ValueCountFrequency (%) 
9999999924131532.1%
 
73991084115< 0.1%
 
739910801< 0.1%
 
739910771< 0.1%
 
739910671< 0.1%
 
7399106516< 0.1%
 
7399106436< 0.1%
 
739910606< 0.1%
 
739910583< 0.1%
 
739910054< 0.1%
 

Penal Code
Categorical

HIGH CARDINALITY

Distinct603
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
No Offense
90479 
PC 30.04(a)
88125 
PC 31.07
57757 
PC 28.03(b)(2)
 
43055
PC 30.02(c)(2)
 
39859
Other values (598)
431587 
ValueCountFrequency (%) 
No Offense9047912.1%
 
PC 30.04(a)8812511.7%
 
PC 31.07577577.7%
 
PC 28.03(b)(2)430555.7%
 
PC 30.02(c)(2)398595.3%
 
PC 30.02(c)(1)269093.6%
 
PC 29.03227083.0%
 
PC 49.02225403.0%
 
No Violation201342.7%
 
PC 31.03(e)(3)185102.5%
 
PC 31.03(e)(2)(Ai)179002.4%
 
PC 31.03(f)163412.2%
 
PC 22.01(a)(3)141591.9%
 
PC 31.03(e)(2)(A)139011.9%
 
PC 28.04(a)136211.8%
 
PC 31.03(e)(4)(A)132531.8%
 
PC 22.01(a)(1)126531.7%
 
PC 22.02(a)(2)116781.6%
 
UCR103801.4%
 
PC 42.07(c)93931.3%
 
PC 28.03(b)(3)82401.1%
 
PC 28.03(b)(1)79681.1%
 
PC 29.0272421.0%
 
PC 28.03(b)(3)(A)69110.9%
 
Alarm- No Offense64900.9%
 
Other values (578)15065620.1%
 
2021-03-12T16:39:54.178091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique104 ?
Unique (%)< 0.1%
2021-03-12T16:39:54.329991image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length28
Median length11
Mean length12.19604135
Min length2

Overview of Unicode Properties

Unique unicode characters63
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
)8266919.0%
 
(8264199.0%
 
07591218.3%
 
7496388.2%
 
C6023486.6%
 
.5970126.5%
 
35730166.3%
 
25702856.2%
 
P5548616.1%
 
e3138773.4%
 
13055203.3%
 
a2316172.5%
 
42210892.4%
 
f2202802.4%
 
o1837462.0%
 
n1576571.7%
 
s1316731.4%
 
N1197071.3%
 
81111881.2%
 
i1098371.2%
 
b1087141.2%
 
O1001451.1%
 
c962061.1%
 
5815710.9%
 
7748870.8%
 
Other values (38)5304395.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number276713230.2%
 
Lowercase Letter178107519.4%
 
Uppercase Letter160077717.5%
 
Close Punctuation8266919.0%
 
Open Punctuation8264199.0%
 
Space Separator7496388.2%
 
Other Punctuation5972406.5%
 
Dash Punctuation85680.1%
 
Other Symbol4< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C60234837.6%
 
P55486134.7%
 
N1197077.5%
 
O1001456.3%
 
A702124.4%
 
S248141.6%
 
V215311.3%
 
R182501.1%
 
H178691.1%
 
I172681.1%
 
T165791.0%
 
U120000.7%
 
B101810.6%
 
M60970.4%
 
F51680.3%
 
D26450.2%
 
L358< 0.1%
 
E280< 0.1%
 
W240< 0.1%
 
G94< 0.1%
 
Y80< 0.1%
 
K50< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
749638100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
075912127.4%
 
357301620.7%
 
257028520.6%
 
130552011.0%
 
42210898.0%
 
81111884.0%
 
5815712.9%
 
7748872.7%
 
9635732.3%
 
668820.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.597012> 99.9%
 
/150< 0.1%
 
,78< 0.1%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(826419100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e31387717.6%
 
a23161713.0%
 
f22028012.4%
 
o18374610.3%
 
n1576578.9%
 
s1316737.4%
 
i1098376.2%
 
b1087146.1%
 
c962065.4%
 
t586383.3%
 
l426382.4%
 
r327411.8%
 
g245361.4%
 
v201771.1%
 
d161750.9%
 
u120320.7%
 
m76790.4%
 
y52670.3%
 
h31660.2%
 
p24540.1%
 
w17600.1%
 
k197< 0.1%
 
j8< 0.1%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)826691100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-8568100.0%
 

Most frequent Other Symbol characters

ValueCountFrequency (%) 
�4100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common577569263.1%
 
Latin338185236.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
C60234817.8%
 
P55486116.4%
 
e3138779.3%
 
a2316176.8%
 
f2202806.5%
 
o1837465.4%
 
n1576574.7%
 
s1316733.9%
 
N1197073.5%
 
i1098373.2%
 
b1087143.2%
 
O1001453.0%
 
c962062.8%
 
A702122.1%
 
t586381.7%
 
l426381.3%
 
r327411.0%
 
S248140.7%
 
g245360.7%
 
V215310.6%
 
v201770.6%
 
R182500.5%
 
H178690.5%
 
I172680.5%
 
T165790.5%
 
Other values (20)859312.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
)82669114.3%
 
(82641914.3%
 
075912113.1%
 
74963813.0%
 
.59701210.3%
 
35730169.9%
 
25702859.9%
 
13055205.3%
 
42210893.8%
 
81111881.9%
 
5815711.4%
 
7748871.3%
 
9635731.1%
 
-85680.1%
 
668820.1%
 
/150< 0.1%
 
,78< 0.1%
 
�4< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII9157540> 99.9%
 
Specials4< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
)8266919.0%
 
(8264199.0%
 
07591218.3%
 
7496388.2%
 
C6023486.6%
 
.5970126.5%
 
35730166.3%
 
25702856.2%
 
P5548616.1%
 
e3138773.4%
 
13055203.3%
 
a2316172.5%
 
42210892.4%
 
f2202802.4%
 
o1837462.0%
 
n1576571.7%
 
s1316731.4%
 
N1197071.3%
 
81111881.2%
 
i1098371.2%
 
b1087141.2%
 
O1001451.1%
 
c962061.1%
 
5815710.9%
 
7748870.8%
 
Other values (37)5304355.8%
 

Most frequent Specials characters

ValueCountFrequency (%) 
�4100.0%
 

UCR Offense Name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct53
Distinct (%)< 0.1%
Missing355587
Missing (%)47.4%
Memory size5.7 MiB
THEFT/BMV
63477 
VANDALISM & CRIM MISCHIEF
55234 
FOUND
36982 
OTHER THEFTS
30592 
UUMV
30381 
Other values (48)
178609 
ValueCountFrequency (%) 
THEFT/BMV634778.5%
 
VANDALISM & CRIM MISCHIEF552347.4%
 
FOUND369824.9%
 
OTHER THEFTS305924.1%
 
UUMV303814.0%
 
BURGLARY-RESIDENCE288413.8%
 
ASSAULT227713.0%
 
BURGLARY-BUSINESS151912.0%
 
ROBBERY-INDIVIDUAL139561.9%
 
ACCIDENT MV114801.5%
 
DRUNK & DISORDERLY114711.5%
 
AGG ASSAULT - NFV84921.1%
 
THEFT/SHOPLIFT82731.1%
 
DISORDERLY CONDUCT70460.9%
 
FRAUD51690.7%
 
INJURED PUBLIC48650.6%
 
SUDDEN DEATH&FOUND BODIES44570.6%
 
LOST42770.6%
 
CRIMINAL TRESPASS38270.5%
 
ROBBERY-BUSINESS35290.5%
 
FORGE & COUNTERFEIT31520.4%
 
OTHERS30690.4%
 
NARCOTICS & DRUGS28350.4%
 
DWI26180.3%
 
EMBEZZLEMENT21370.3%
 
Other values (28)111531.5%
 
(Missing)35558747.4%
 
2021-03-12T16:39:54.500433image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:54.664708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length4
Mean length8.320146711
Min length3

Overview of Unicode Properties

Unique unicode characters32
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n71117411.4%
 
E3990086.4%
 
I3945046.3%
 
a3555875.7%
 
S3541485.7%
 
T3319455.3%
 
R3305755.3%
 
3024954.8%
 
A2839774.5%
 
M2838804.5%
 
D2594824.2%
 
U2569834.1%
 
F2320833.7%
 
N2275123.6%
 
H2082163.3%
 
C1998653.2%
 
L1906783.1%
 
V1857413.0%
 
B1740282.8%
 
O1525812.4%
 
Y805291.3%
 
&771621.2%
 
/717501.1%
 
-700391.1%
 
G692701.1%
 
Other values (7)440700.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter465907574.6%
 
Lowercase Letter106676117.1%
 
Space Separator3024954.8%
 
Other Punctuation1489122.4%
 
Dash Punctuation700391.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n71117466.7%
 
a35558733.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E3990088.6%
 
I3945048.5%
 
S3541487.6%
 
T3319457.1%
 
R3305757.1%
 
A2839776.1%
 
M2838806.1%
 
D2594825.6%
 
U2569835.5%
 
F2320835.0%
 
N2275124.9%
 
H2082164.5%
 
C1998654.3%
 
L1906784.1%
 
V1857414.0%
 
B1740283.7%
 
O1525813.3%
 
Y805291.7%
 
G692701.5%
 
P179710.4%
 
K114930.2%
 
J69820.1%
 
Z42880.1%
 
W32660.1%
 
Q38< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
302495100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
&7716251.8%
 
/7175048.2%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-70039100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin572583691.7%
 
Common5214468.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n71117412.4%
 
E3990087.0%
 
I3945046.9%
 
a3555876.2%
 
S3541486.2%
 
T3319455.8%
 
R3305755.8%
 
A2839775.0%
 
M2838805.0%
 
D2594824.5%
 
U2569834.5%
 
F2320834.1%
 
N2275124.0%
 
H2082163.6%
 
C1998653.5%
 
L1906783.3%
 
V1857413.2%
 
B1740283.0%
 
O1525812.7%
 
Y805291.4%
 
G692701.2%
 
P179710.3%
 
K114930.2%
 
J69820.1%
 
Z42880.1%
 
Other values (3)33360.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
30249558.0%
 
&7716214.8%
 
/7175013.8%
 
-7003913.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6247282100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n71117411.4%
 
E3990086.4%
 
I3945046.3%
 
a3555875.7%
 
S3541485.7%
 
T3319455.3%
 
R3305755.3%
 
3024954.8%
 
A2839774.5%
 
M2838804.5%
 
D2594824.2%
 
U2569834.1%
 
F2320833.7%
 
N2275123.6%
 
H2082163.3%
 
C1998653.2%
 
L1906783.1%
 
V1857413.0%
 
B1740282.8%
 
O1525812.4%
 
Y805291.3%
 
&771621.2%
 
/717501.1%
 
-700391.1%
 
G692701.1%
 
Other values (7)440700.7%
 

Special Report (Pre-RMS)
Categorical

MISSING

Distinct15
Distinct (%)3.4%
Missing750425
Missing (%)99.9%
Memory size5.7 MiB
Protest
139 
RMS-System Dark
111 
Alan Ross Texas Freedom Parade
73 
Fireworks At River Bottoms
40 
Fireworks At State Fair
18 
Other values (10)
56 
ValueCountFrequency (%) 
Protest139< 0.1%
 
RMS-System Dark111< 0.1%
 
Alan Ross Texas Freedom Parade73< 0.1%
 
Fireworks At River Bottoms40< 0.1%
 
Fireworks At State Fair18< 0.1%
 
Mary Kay Convention11< 0.1%
 
Martin Luther King Jr. Parade10< 0.1%
 
State Fair (Inside Fair)10< 0.1%
 
State Fair (Outside Fair)7< 0.1%
 
MegaFest5< 0.1%
 
SC DORS Referral4< 0.1%
 
State Fair4< 0.1%
 
RMS-Days Prior To Upgrade3< 0.1%
 
MEGAFE1< 0.1%
 
St. Patrick's Day Parade1< 0.1%
 
(Missing)75042599.9%
 
2021-03-12T16:39:54.817006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.5%
2021-03-12T16:39:54.969363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length3
Mean length3.008169277
Min length3

Overview of Unicode Properties

Unique unicode characters46
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n150098666.5%
 
a75099433.2%
 
e749< 0.1%
 
714< 0.1%
 
r678< 0.1%
 
t650< 0.1%
 
s593< 0.1%
 
o451< 0.1%
 
S273< 0.1%
 
R235< 0.1%
 
P227< 0.1%
 
m224< 0.1%
 
i206< 0.1%
 
F193< 0.1%
 
d177< 0.1%
 
k170< 0.1%
 
M141< 0.1%
 
y137< 0.1%
 
A132< 0.1%
 
D119< 0.1%
 
-114< 0.1%
 
l77< 0.1%
 
T76< 0.1%
 
x73< 0.1%
 
w58< 0.1%
 
Other values (21)273< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter225632799.9%
 
Uppercase Letter15190.1%
 
Space Separator714< 0.1%
 
Dash Punctuation114< 0.1%
 
Open Punctuation17< 0.1%
 
Close Punctuation17< 0.1%
 
Other Punctuation12< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n150098666.5%
 
a75099433.3%
 
e749< 0.1%
 
r678< 0.1%
 
t650< 0.1%
 
s593< 0.1%
 
o451< 0.1%
 
m224< 0.1%
 
i206< 0.1%
 
d177< 0.1%
 
k170< 0.1%
 
y137< 0.1%
 
l77< 0.1%
 
x73< 0.1%
 
w58< 0.1%
 
v51< 0.1%
 
g18< 0.1%
 
u17< 0.1%
 
h10< 0.1%
 
f4< 0.1%
 
p3< 0.1%
 
c1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S27318.0%
 
R23515.5%
 
P22714.9%
 
F19312.7%
 
M1419.3%
 
A1328.7%
 
D1197.8%
 
T765.0%
 
B402.6%
 
K211.4%
 
C151.0%
 
O110.7%
 
L100.7%
 
J100.7%
 
I100.7%
 
U30.2%
 
E20.1%
 
G10.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
714100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1191.7%
 
'18.3%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(17100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)17100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-114100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2257846> 99.9%
 
Common874< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n150098666.5%
 
a75099433.3%
 
e749< 0.1%
 
r678< 0.1%
 
t650< 0.1%
 
s593< 0.1%
 
o451< 0.1%
 
S273< 0.1%
 
R235< 0.1%
 
P227< 0.1%
 
m224< 0.1%
 
i206< 0.1%
 
F193< 0.1%
 
d177< 0.1%
 
k170< 0.1%
 
M141< 0.1%
 
y137< 0.1%
 
A132< 0.1%
 
D119< 0.1%
 
l77< 0.1%
 
T76< 0.1%
 
x73< 0.1%
 
w58< 0.1%
 
v51< 0.1%
 
B40< 0.1%
 
Other values (15)136< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
71481.7%
 
-11413.0%
 
(171.9%
 
)171.9%
 
.111.3%
 
'10.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2258720100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n150098666.5%
 
a75099433.2%
 
e749< 0.1%
 
714< 0.1%
 
r678< 0.1%
 
t650< 0.1%
 
s593< 0.1%
 
o451< 0.1%
 
S273< 0.1%
 
R235< 0.1%
 
P227< 0.1%
 
m224< 0.1%
 
i206< 0.1%
 
F193< 0.1%
 
d177< 0.1%
 
k170< 0.1%
 
M141< 0.1%
 
y137< 0.1%
 
A132< 0.1%
 
D119< 0.1%
 
-114< 0.1%
 
l77< 0.1%
 
T76< 0.1%
 
x73< 0.1%
 
w58< 0.1%
 
Other values (21)273< 0.1%
 

UCR Offense Description
Categorical

HIGH CORRELATION
MISSING

Distinct46
Distinct (%)< 0.1%
Missing355587
Missing (%)47.4%
Memory size5.7 MiB
THEFT
102342 
CRIMINAL MISCHIEF/VANDALISM
54722 
BURGLARY
44032 
FOUND PROPERTY
36982 
AUTO THEFT - UUMV
27818 
Other values (41)
129379 
ValueCountFrequency (%) 
THEFT10234213.6%
 
CRIMINAL MISCHIEF/VANDALISM547227.3%
 
BURGLARY440325.9%
 
FOUND PROPERTY369824.9%
 
AUTO THEFT - UUMV278183.7%
 
ASSAULT237013.2%
 
ROBBERY174852.3%
 
MOTOR VEHICLE ACCIDENT123341.6%
 
DRUNK & DISORDERLY114711.5%
 
OTHER OFFENSES100591.3%
 
AGGRAVATED ASSAULT84711.1%
 
DISORDERLY CONDUCT65040.9%
 
FRAUD51550.7%
 
ACCIDENTAL INJURY48650.6%
 
SUDDEN DEATH44570.6%
 
LOST PROPERTY42770.6%
 
FORGERY & COUNTERFEIT29790.4%
 
NARCOTICS/DRUGS28150.4%
 
DWI26390.4%
 
MOTOR VEHICLE THEFT25630.3%
 
EMBEZZLEMENT21370.3%
 
HOME ACCIDENT15590.2%
 
ANIMAL BITE13390.2%
 
ARSON9040.1%
 
WEAPONS7060.1%
 
Other values (21)29590.4%
 
(Missing)35558747.4%
 
2021-03-12T16:39:55.132211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2021-03-12T16:39:55.295705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length28
Median length5
Mean length8.086339434
Min length3

Overview of Unicode Properties

Unique unicode characters34
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n71117411.7%
 
T4504477.4%
 
E3853796.3%
 
A3706266.1%
 
R3704736.1%
 
a3555875.9%
 
I3454075.7%
 
2784764.6%
 
F2598894.3%
 
U2361373.9%
 
L2325943.8%
 
S2297353.8%
 
H2188353.6%
 
O2161903.6%
 
N2160363.6%
 
M2150633.5%
 
D1985063.3%
 
C1868133.1%
 
Y1293112.1%
 
V1059161.7%
 
P838391.4%
 
B827831.4%
 
G676661.1%
 
/575860.9%
 
-278180.5%
 
Other values (9)394390.6%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter462570176.2%
 
Lowercase Letter106676117.6%
 
Space Separator2784764.6%
 
Other Punctuation722291.2%
 
Dash Punctuation278180.5%
 
Open Punctuation370< 0.1%
 
Close Punctuation370< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n71117466.7%
 
a35558733.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T4504479.7%
 
E3853798.3%
 
A3706268.0%
 
R3704738.0%
 
I3454077.5%
 
F2598895.6%
 
U2361375.1%
 
L2325945.0%
 
S2297355.0%
 
H2188354.7%
 
O2161904.7%
 
N2160364.7%
 
M2150634.6%
 
D1985064.3%
 
C1868134.0%
 
Y1293112.8%
 
V1059162.3%
 
P838391.8%
 
B827831.8%
 
G676661.5%
 
K115050.2%
 
J48650.1%
 
Z42740.1%
 
W33450.1%
 
Q38< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
278476100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/5758679.7%
 
&1464320.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-27818100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(370100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)370100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin569246293.8%
 
Common3792636.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n71117412.5%
 
T4504477.9%
 
E3853796.8%
 
A3706266.5%
 
R3704736.5%
 
a3555876.2%
 
I3454076.1%
 
F2598894.6%
 
U2361374.1%
 
L2325944.1%
 
S2297354.0%
 
H2188353.8%
 
O2161903.8%
 
N2160363.8%
 
M2150633.8%
 
D1985063.5%
 
C1868133.3%
 
Y1293112.3%
 
V1059161.9%
 
P838391.5%
 
B827831.5%
 
G676661.2%
 
K115050.2%
 
J48650.1%
 
Z42740.1%
 
Other values (3)34120.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
27847673.4%
 
/5758615.2%
 
-278187.3%
 
&146433.9%
 
(3700.1%
 
)3700.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6071725100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n71117411.7%
 
T4504477.4%
 
E3853796.3%
 
A3706266.1%
 
R3704736.1%
 
a3555875.9%
 
I3454075.7%
 
2784764.6%
 
F2598894.3%
 
U2361373.9%
 
L2325943.8%
 
S2297353.8%
 
H2188353.6%
 
O2161903.6%
 
N2160363.6%
 
M2150633.5%
 
D1985063.3%
 
C1868133.1%
 
Y1293112.1%
 
V1059161.7%
 
P838391.4%
 
B827831.4%
 
G676661.1%
 
/575860.9%
 
-278180.5%
 
Other values (9)394390.6%
 

UCR Code
Real number (ℝ≥0)

MISSING

Distinct66
Distinct (%)< 0.1%
Missing355587
Missing (%)47.4%
Infinite0
Infinite (%)0.0%
Mean1408.482338
Minimum110
Maximum5700
Zeros0
Zeros (%)0.0%
Memory size5.7 MiB
2021-03-12T16:39:55.437536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile400
Q1630
median710
Q32000
95-th percentile4300
Maximum5700
Range5590
Interquartile range (IQR)1370

Descriptive statistics

Standard deviation1230.784064
Coefficient of variation (CV)0.8738370595
Kurtosis0.451015381
Mean1408.482338
Median Absolute Deviation (MAD)260
Skewness1.317448269
Sum556737856
Variance1514829.413
MonotocityNot monotonic
2021-03-12T16:39:55.580046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1400552347.4%
 
640491926.6%
 
4300295113.9%
 
690274663.7%
 
450228253.0%
 
710227093.0%
 
511194682.6%
 
2600179902.4%
 
300174852.3%
 
650142111.9%
 
3200129441.7%
 
512123431.6%
 
2300114711.5%
 
52186971.2%
 
40085181.1%
 
63082721.1%
 
240065020.9%
 
72064510.9%
 
110051520.7%
 
330048650.6%
 
400044570.6%
 
420042770.6%
 
100031690.4%
 
180027240.4%
 
52226650.4%
 
Other values (41)166772.2%
 
(Missing)35558747.4%
 
ValueCountFrequency (%) 
1104250.1%
 
1201< 0.1%
 
300174852.3%
 
40085181.1%
 
450228253.0%
 
511194682.6%
 
512123431.6%
 
52186971.2%
 
52226650.4%
 
5316760.1%
 
ValueCountFrequency (%) 
570027< 0.1%
 
56004820.1%
 
44001< 0.1%
 
4300295113.9%
 
420042770.6%
 
400044570.6%
 
3900107< 0.1%
 
370013390.2%
 
3600375< 0.1%
 
3500183< 0.1%
 

Victim Name
Categorical

HIGH CARDINALITY
MISSING

Distinct411413
Distinct (%)57.1%
Missing30862
Missing (%)4.1%
Memory size5.7 MiB
CITY OF DALLAS
106063 
7 Eleven
 
4790
DALLAS POLICE DEPARTMENT
 
4451
Walmart
 
2776
Family Dollar
 
1512
Other values (411408)
600408 
ValueCountFrequency (%) 
CITY OF DALLAS10606314.1%
 
7 Eleven47900.6%
 
DALLAS POLICE DEPARTMENT44510.6%
 
Walmart27760.4%
 
Family Dollar15120.2%
 
Quik Trip11180.1%
 
CITY OF DALLAS (SOCIETY)11030.1%
 
Target10880.1%
 
CVS10440.1%
 
ONCOR9930.1%
 
Shell8660.1%
 
McDonalds7080.1%
 
Walgreens6990.1%
 
Metro PCS6430.1%
 
MESQUITE PD6280.1%
 
Home Depot5890.1%
 
DALLAS FIRE RESCUE5470.1%
 
AT&T5300.1%
 
CITY OF GARLAND5130.1%
 
Race Trac5020.1%
 
CITY DALLAS4750.1%
 
Dollar Genreal4680.1%
 
THE CITY OF DALLAS4630.1%
 
CITY OF MESQUITE4610.1%
 
IRVING PD4450.1%
 
Other values (411388)58652578.1%
 
(Missing)308624.1%
 
2021-03-12T16:39:57.970869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique338748 ?
Unique (%)47.0%
2021-03-12T16:39:58.184399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length54
Median length15
Mean length15.69237357
Min length1

Overview of Unicode Properties

Unique unicode characters90
Unique unicode categories13 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A124467710.6%
 
10515288.9%
 
E9165807.8%
 
L7612776.5%
 
R7132526.1%
 
I6871365.8%
 
O6700585.7%
 
N6571495.6%
 
,6056275.1%
 
S6012585.1%
 
T5255634.5%
 
C4202853.6%
 
D4170343.5%
 
M2863332.4%
 
Y2788962.4%
 
H2682632.3%
 
U1958961.7%
 
F1918421.6%
 
G1598761.4%
 
P1467951.2%
 
B1421101.2%
 
K1104710.9%
 
J1073990.9%
 
W976390.8%
 
Z945790.8%
 
Other values (65)4312843.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter982361583.4%
 
Space Separator10515288.9%
 
Other Punctuation6242545.3%
 
Lowercase Letter2361522.0%
 
Decimal Number282390.2%
 
Dash Punctuation150340.1%
 
Close Punctuation1841< 0.1%
 
Open Punctuation1839< 0.1%
 
Modifier Symbol180< 0.1%
 
Other Symbol52< 0.1%
 
Math Symbol49< 0.1%
 
Currency Symbol23< 0.1%
 
Connector Punctuation1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n7127330.2%
 
a4824520.4%
 
e217809.2%
 
l189958.0%
 
r130215.5%
 
o102904.4%
 
t75533.2%
 
i69713.0%
 
m57292.4%
 
v51852.2%
 
s45031.9%
 
g32791.4%
 
c30961.3%
 
u25421.1%
 
h24701.0%
 
p22591.0%
 
k21780.9%
 
y20400.9%
 
x15640.7%
 
b9130.4%
 
z7920.3%
 
d7240.3%
 
w3940.2%
 
f3560.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A124467712.7%
 
E9165809.3%
 
L7612777.7%
 
R7132527.3%
 
I6871367.0%
 
O6700586.8%
 
N6571496.7%
 
S6012586.1%
 
T5255635.3%
 
C4202854.3%
 
D4170344.2%
 
M2863332.9%
 
Y2788962.8%
 
H2682632.7%
 
U1958962.0%
 
F1918422.0%
 
G1598761.6%
 
P1467951.5%
 
B1421101.4%
 
K1104711.1%
 
J1073991.1%
 
W976391.0%
 
Z945791.0%
 
V942681.0%
 
Q197050.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1051528100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,60562797.0%
 
'54580.9%
 
#41500.7%
 
.41090.7%
 
&30020.5%
 
@6620.1%
 
/5350.1%
 
*5060.1%
 
"73< 0.1%
 
;45< 0.1%
 
:43< 0.1%
 
!14< 0.1%
 
%13< 0.1%
 
?9< 0.1%
 
\8< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-15034100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
7725325.7%
 
1381813.5%
 
2290610.3%
 
027159.6%
 
623008.1%
 
522167.8%
 
420227.2%
 
318916.7%
 
915865.6%
 
815325.4%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(183899.9%
 
[10.1%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)183699.7%
 
]50.3%
 

Most frequent Other Symbol characters

ValueCountFrequency (%) 
�52100.0%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`180100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+3571.4%
 
=612.2%
 
~48.2%
 
<24.1%
 
>24.1%
 

Most frequent Currency Symbol characters

ValueCountFrequency (%) 
$23100.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1005976785.4%
 
Common172304014.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A124467712.4%
 
E9165809.1%
 
L7612777.6%
 
R7132527.1%
 
I6871366.8%
 
O6700586.7%
 
N6571496.5%
 
S6012586.0%
 
T5255635.2%
 
C4202854.2%
 
D4170344.1%
 
M2863332.8%
 
Y2788962.8%
 
H2682632.7%
 
U1958961.9%
 
F1918421.9%
 
G1598761.6%
 
P1467951.5%
 
B1421101.4%
 
K1104711.1%
 
J1073991.1%
 
W976391.0%
 
Z945790.9%
 
V942680.9%
 
n712730.7%
 
Other values (25)1998582.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
105152861.0%
 
,60562735.1%
 
-150340.9%
 
772530.4%
 
'54580.3%
 
#41500.2%
 
.41090.2%
 
138180.2%
 
&30020.2%
 
229060.2%
 
027150.2%
 
623000.1%
 
522160.1%
 
420220.1%
 
318910.1%
 
(18380.1%
 
)18360.1%
 
915860.1%
 
815320.1%
 
@662< 0.1%
 
/535< 0.1%
 
*506< 0.1%
 
`180< 0.1%
 
"73< 0.1%
 
�52< 0.1%
 
Other values (15)211< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII11782755> 99.9%
 
Specials52< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A124467710.6%
 
10515288.9%
 
E9165807.8%
 
L7612776.5%
 
R7132526.1%
 
I6871365.8%
 
O6700585.7%
 
N6571495.6%
 
,6056275.1%
 
S6012585.1%
 
T5255634.5%
 
C4202853.6%
 
D4170343.5%
 
M2863332.4%
 
Y2788962.4%
 
H2682632.3%
 
U1958961.7%
 
F1918421.6%
 
G1598761.4%
 
P1467951.2%
 
B1421101.2%
 
K1104710.9%
 
J1073990.9%
 
W976390.8%
 
Z945790.8%
 
Other values (64)4312323.7%
 

Most frequent Specials characters

ValueCountFrequency (%) 
�52100.0%
 

Offense Type
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing355587
Missing (%)47.4%
Memory size5.7 MiB
PART1
196071 
PART2
138516 
NOT CODED
60688 
ValueCountFrequency (%) 
PART119607126.1%
 
PART213851618.4%
 
NOT CODED606888.1%
 
(Missing)35558747.4%
 
2021-03-12T16:39:58.346576image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:58.448196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:39:58.570398image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length5
Mean length4.37615434
Min length3

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n71117421.6%
 
T39527512.0%
 
a35558710.8%
 
P33458710.2%
 
A33458710.2%
 
R33458710.2%
 
11960716.0%
 
21385164.2%
 
O1213763.7%
 
D1213763.7%
 
N606881.8%
 
606881.8%
 
C606881.8%
 
E606881.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter182385255.5%
 
Lowercase Letter106676132.5%
 
Decimal Number33458710.2%
 
Space Separator606881.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n71117466.7%
 
a35558733.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T39527521.7%
 
P33458718.3%
 
A33458718.3%
 
R33458718.3%
 
O1213766.7%
 
D1213766.7%
 
N606883.3%
 
C606883.3%
 
E606883.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
60688100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
119607158.6%
 
213851641.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin289061388.0%
 
Common39527512.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n71117424.6%
 
T39527513.7%
 
a35558712.3%
 
P33458711.6%
 
A33458711.6%
 
R33458711.6%
 
O1213764.2%
 
D1213764.2%
 
N606882.1%
 
C606882.1%
 
E606882.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
119607149.6%
 
213851635.0%
 
6068815.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3285888100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n71117421.6%
 
T39527512.0%
 
a35558710.8%
 
P33458710.2%
 
A33458710.2%
 
R33458710.2%
 
11960716.0%
 
21385164.2%
 
O1213763.7%
 
D1213763.7%
 
N606881.8%
 
606881.8%
 
C606881.8%
 
E606881.8%
 

NIBRS Crime
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct55
Distinct (%)< 0.1%
Missing249799
Missing (%)33.3%
Memory size5.7 MiB
MISCELLANEOUS
120100 
THEFT FROM MOTOR VEHICLE
50790 
DESTRUCTION/ DAMAGE/ VANDALISM OF PROPERTY
43475 
UUMV
39466 
ALL OTHER LARCENY
32909 
Other values (50)
214323 
ValueCountFrequency (%) 
MISCELLANEOUS12010016.0%
 
THEFT FROM MOTOR VEHICLE507906.8%
 
DESTRUCTION/ DAMAGE/ VANDALISM OF PROPERTY434755.8%
 
UUMV394665.3%
 
ALL OTHER LARCENY329094.4%
 
BURGLARY-RESIDENCE197362.6%
 
BURGLARY-BUSINESS178292.4%
 
SIMPLE ASSAULT173572.3%
 
ALL OTHER OFFENSES169932.3%
 
PUBLIC INTOXICATION156872.1%
 
THEFT OF MOTOR VEHICLE PARTS OR ACCESSORIES154832.1%
 
AGG ASSAULT - NFV134781.8%
 
TRAFFIC VIOLATION - HAZARDOUS134671.8%
 
ROBBERY-INDIVIDUAL130231.7%
 
DRUG/ NARCOTIC VIOLATIONS121281.6%
 
INTIMIDATION118091.6%
 
SHOPLIFTING79491.1%
 
ROBBERY-BUSINESS57080.8%
 
DUI53860.7%
 
TRESPASS OF REAL PROPERTY45070.6%
 
WEAPON LAW VIOLATIONS37540.5%
 
FALSE PRETENSES/ SWINDLE/ CONFIDENCE GAME29780.4%
 
FAMILY OFFENSES, NONVIOLENT29290.4%
 
COUNTERFEITING / FORGERY25440.3%
 
EMBEZZELMENT17240.2%
 
Other values (30)98541.3%
 
(Missing)24979933.3%
 
2021-03-12T16:39:58.742930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2021-03-12T16:39:58.915607image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length43
Median length13
Mean length13.85326731
Min length3

Overview of Unicode Properties

Unique unicode characters33
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
E9138478.8%
 
O7428927.1%
 
7173766.9%
 
A6822216.6%
 
S6669066.4%
 
L6621406.4%
 
I6360876.1%
 
R6026025.8%
 
T5816835.6%
 
n4995984.8%
 
N4792544.6%
 
M4070123.9%
 
U4037943.9%
 
C3999003.8%
 
F2889132.8%
 
a2497992.4%
 
D2361942.3%
 
V2096482.0%
 
H2077812.0%
 
P1674411.6%
 
Y1457671.4%
 
G1404121.3%
 
B1171331.1%
 
/1094761.1%
 
-851570.8%
 
Other values (8)488590.5%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter873713984.0%
 
Lowercase Letter7493977.2%
 
Space Separator7173766.9%
 
Other Punctuation1128231.1%
 
Dash Punctuation851570.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E91384710.5%
 
O7428928.5%
 
A6822217.8%
 
S6669067.6%
 
L6621407.6%
 
I6360877.3%
 
R6026026.9%
 
T5816836.7%
 
N4792545.5%
 
M4070124.7%
 
U4037944.6%
 
C3999004.6%
 
F2889133.3%
 
D2361942.7%
 
V2096482.4%
 
H2077812.4%
 
P1674411.9%
 
Y1457671.7%
 
G1404121.6%
 
B1171331.3%
 
Z175940.2%
 
X157120.2%
 
W106230.1%
 
K1023< 0.1%
 
Q530< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
717376100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/10947697.0%
 
,29422.6%
 
&4050.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n49959866.7%
 
a24979933.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-85157100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin948653691.2%
 
Common9153568.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E9138479.6%
 
O7428927.8%
 
A6822217.2%
 
S6669067.0%
 
L6621407.0%
 
I6360876.7%
 
R6026026.4%
 
T5816836.1%
 
n4995985.3%
 
N4792545.1%
 
M4070124.3%
 
U4037944.3%
 
C3999004.2%
 
F2889133.0%
 
a2497992.6%
 
D2361942.5%
 
V2096482.2%
 
H2077812.2%
 
P1674411.8%
 
Y1457671.5%
 
G1404121.5%
 
B1171331.2%
 
Z175940.2%
 
X157120.2%
 
W106230.1%
 
Other values (3)1583< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
71737678.4%
 
/10947612.0%
 
-851579.3%
 
,29420.3%
 
&405< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII10401892100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
E9138478.8%
 
O7428927.1%
 
7173766.9%
 
A6822216.6%
 
S6669066.4%
 
L6621406.4%
 
I6360876.1%
 
R6026025.8%
 
T5816835.6%
 
n4995984.8%
 
N4792544.6%
 
M4070123.9%
 
U4037943.9%
 
C3999003.8%
 
F2889132.8%
 
a2497992.4%
 
D2361942.3%
 
V2096482.0%
 
H2077812.0%
 
P1674411.6%
 
Y1457671.4%
 
G1404121.3%
 
B1171331.1%
 
/1094761.1%
 
-851570.8%
 
Other values (8)488590.5%
 

NIBRS Crime Category
Categorical

HIGH CORRELATION
MISSING

Distinct33
Distinct (%)< 0.1%
Missing249799
Missing (%)33.3%
Memory size5.7 MiB
MISCELLANEOUS
120100 
LARCENY/ THEFT OFFENSES
108913 
DESTRUCTION/ DAMAGE/ VANDALISM OF PROPERTY
43475 
ASSAULT OFFENSES
42775 
MOTOR VEHICLE THEFT
39466 
Other values (28)
146334 
ValueCountFrequency (%) 
MISCELLANEOUS12010016.0%
 
LARCENY/ THEFT OFFENSES10891314.5%
 
DESTRUCTION/ DAMAGE/ VANDALISM OF PROPERTY434755.8%
 
ASSAULT OFFENSES427755.7%
 
MOTOR VEHICLE THEFT394665.3%
 
BURGLARY/ BREAKING & ENTERING375655.0%
 
ROBBERY187312.5%
 
ALL OTHER OFFENSES169932.3%
 
PUBLIC INTOXICATION156872.1%
 
TRAFFIC VIOLATION - HAZARDOUS134671.8%
 
DRUG/ NARCOTIC VIOLATIONS126471.7%
 
FRAUD OFFENSES60800.8%
 
DRIVING UNDER THE INFLUENCE53860.7%
 
TRESPASS OF REAL PROPERTY45070.6%
 
WEAPON LAW VIOLATIONS37540.5%
 
FAMILY OFFENSES, NONVIOLENT29290.4%
 
COUNTERFEITING / FORGERY25440.3%
 
EMBEZZELMENT17240.2%
 
DISORDERLY CONDUCT9630.1%
 
ANIMAL OFFENSES8310.1%
 
ARSON7600.1%
 
TRAFFIC VIOLATION - NON HAZARDOUS6790.1%
 
HOMICIDE OFFENSES4330.1%
 
PORNOGRAPHY/ OBSCENE MATERIAL182< 0.1%
 
STOLEN PROPERTY OFFENSES170< 0.1%
 
Other values (8)302< 0.1%
 
(Missing)24979933.3%
 
2021-03-12T16:39:59.098220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:59.271206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length42
Median length13
Mean length15.00692138
Min length3

Overview of Unicode Properties

Unique unicode characters32
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
E127241511.3%
 
S8173967.3%
 
8016657.1%
 
N7283466.5%
 
A7108146.3%
 
O6922786.1%
 
T6784776.0%
 
L6161805.5%
 
F6027045.3%
 
R5832465.2%
 
I5027814.5%
 
n4995984.4%
 
C3933703.5%
 
U3068832.7%
 
M2545092.3%
 
a2497992.2%
 
/2489132.2%
 
H2249982.0%
 
Y2199892.0%
 
G1798791.6%
 
D1776021.6%
 
B1304611.2%
 
V1218141.1%
 
P1208501.1%
 
K376900.3%
 
Other values (7)954700.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter941350683.5%
 
Space Separator8016657.1%
 
Lowercase Letter7493976.7%
 
Other Punctuation2894132.6%
 
Dash Punctuation141460.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E127241513.5%
 
S8173968.7%
 
N7283467.7%
 
A7108147.6%
 
O6922787.4%
 
T6784777.2%
 
L6161806.5%
 
F6027046.4%
 
R5832466.2%
 
I5027815.3%
 
C3933704.2%
 
U3068833.3%
 
M2545092.7%
 
H2249982.4%
 
Y2199892.3%
 
G1798791.9%
 
D1776021.9%
 
B1304611.4%
 
V1218141.3%
 
P1208501.3%
 
K376900.4%
 
Z175940.2%
 
X157000.2%
 
W75190.1%
 
Q11< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
801665100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/24891386.0%
 
&3756513.0%
 
,29351.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n49959866.7%
 
a24979933.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-14146100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1016290390.2%
 
Common11052249.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E127241512.5%
 
S8173968.0%
 
N7283467.2%
 
A7108147.0%
 
O6922786.8%
 
T6784776.7%
 
L6161806.1%
 
F6027045.9%
 
R5832465.7%
 
I5027814.9%
 
n4995984.9%
 
C3933703.9%
 
U3068833.0%
 
M2545092.5%
 
a2497992.5%
 
H2249982.2%
 
Y2199892.2%
 
G1798791.8%
 
D1776021.7%
 
B1304611.3%
 
V1218141.2%
 
P1208501.2%
 
K376900.4%
 
Z175940.2%
 
X157000.2%
 
Other values (2)75300.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
80166572.5%
 
/24891322.5%
 
&375653.4%
 
-141461.3%
 
,29350.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII11268127100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
E127241511.3%
 
S8173967.3%
 
8016657.1%
 
N7283466.5%
 
A7108146.3%
 
O6922786.1%
 
T6784776.0%
 
L6161805.5%
 
F6027045.3%
 
R5832465.2%
 
I5027814.5%
 
n4995984.4%
 
C3933703.5%
 
U3068832.7%
 
M2545092.3%
 
a2497992.2%
 
/2489132.2%
 
H2249982.0%
 
Y2199892.0%
 
G1798791.6%
 
D1776021.6%
 
B1304611.2%
 
V1218141.1%
 
P1208501.1%
 
K376900.3%
 
Other values (7)954700.8%
 

Victim Ethnicity
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing277767
Missing (%)37.0%
Memory size5.7 MiB
Non-Hispanic or Latino
329543 
Hispanic or Latino
142041 
Unknown
 
1511
ValueCountFrequency (%) 
Non-Hispanic or Latino32954343.9%
 
Hispanic or Latino14204118.9%
 
Unknown15110.2%
 
(Missing)27776737.0%
 
2021-03-12T16:39:59.413315image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:59.504725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:39:59.654842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length18
Mean length14.18444668
Min length3

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n183277817.2%
 
i141475213.3%
 
o127422212.0%
 
a122093511.5%
 
9431688.9%
 
H4715844.4%
 
s4715844.4%
 
p4715844.4%
 
c4715844.4%
 
r4715844.4%
 
L4715844.4%
 
t4715844.4%
 
N3295433.1%
 
-3295433.1%
 
U1511< 0.1%
 
k1511< 0.1%
 
w1511< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter810362976.1%
 
Uppercase Letter127422212.0%
 
Space Separator9431688.9%
 
Dash Punctuation3295433.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n183277822.6%
 
i141475217.5%
 
o127422215.7%
 
a122093515.1%
 
s4715845.8%
 
p4715845.8%
 
c4715845.8%
 
r4715845.8%
 
t4715845.8%
 
k1511< 0.1%
 
w1511< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
H47158437.0%
 
L47158437.0%
 
N32954325.9%
 
U15110.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-329543100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
943168100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin937785188.1%
 
Common127271111.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n183277819.5%
 
i141475215.1%
 
o127422213.6%
 
a122093513.0%
 
H4715845.0%
 
s4715845.0%
 
p4715845.0%
 
c4715845.0%
 
r4715845.0%
 
L4715845.0%
 
t4715845.0%
 
N3295433.5%
 
U1511< 0.1%
 
k1511< 0.1%
 
w1511< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
94316874.1%
 
-32954325.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII10650562100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n183277817.2%
 
i141475213.3%
 
o127422212.0%
 
a122093511.5%
 
9431688.9%
 
H4715844.4%
 
s4715844.4%
 
p4715844.4%
 
c4715844.4%
 
r4715844.4%
 
L4715844.4%
 
t4715844.4%
 
N3295433.1%
 
-3295433.1%
 
U1511< 0.1%
 
k1511< 0.1%
 
w1511< 0.1%
 

NIBRS Crime Against
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing249799
Missing (%)33.3%
Memory size5.7 MiB
PROPERTY
259445 
MISCELLANEOUS
120100 
SOCIETY
47047 
PERSON
43332 
PERSON, PROPERTY, OR SOCIETY
31139 
ValueCountFrequency (%) 
PROPERTY25944534.6%
 
MISCELLANEOUS12010016.0%
 
SOCIETY470476.3%
 
PERSON433325.8%
 
PERSON, PROPERTY, OR SOCIETY311394.1%
 
(Missing)24979933.3%
 
2021-03-12T16:39:59.788975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:39:59.880655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:40:00.052907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length28
Median length8
Mean length7.787676031
Min length3

Overview of Unicode Properties

Unique unicode characters18
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
R68677811.7%
 
E68344111.7%
 
P65563911.2%
 
O59448010.2%
 
n4995988.5%
 
S3928576.7%
 
T3687706.3%
 
Y3687706.3%
 
a2497994.3%
 
L2402004.1%
 
C1982863.4%
 
I1982863.4%
 
N1945713.3%
 
M1201002.1%
 
A1201002.1%
 
U1201002.1%
 
934171.6%
 
,622781.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter494237884.5%
 
Lowercase Letter74939712.8%
 
Space Separator934171.6%
 
Other Punctuation622781.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R68677813.9%
 
E68344113.8%
 
P65563913.3%
 
O59448012.0%
 
S3928577.9%
 
T3687707.5%
 
Y3687707.5%
 
L2402004.9%
 
C1982864.0%
 
I1982864.0%
 
N1945713.9%
 
M1201002.4%
 
A1201002.4%
 
U1201002.4%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,62278100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
93417100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n49959866.7%
 
a24979933.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin569177597.3%
 
Common1556952.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R68677812.1%
 
E68344112.0%
 
P65563911.5%
 
O59448010.4%
 
n4995988.8%
 
S3928576.9%
 
T3687706.5%
 
Y3687706.5%
 
a2497994.4%
 
L2402004.2%
 
C1982863.5%
 
I1982863.5%
 
N1945713.4%
 
M1201002.1%
 
A1201002.1%
 
U1201002.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
9341760.0%
 
,6227840.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5847470100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
R68677811.7%
 
E68344111.7%
 
P65563911.2%
 
O59448010.2%
 
n4995988.5%
 
S3928576.7%
 
T3687706.3%
 
Y3687706.3%
 
a2497994.3%
 
L2402004.1%
 
C1982863.4%
 
I1982863.4%
 
N1945713.3%
 
M1201002.1%
 
A1201002.1%
 
U1201002.1%
 
934171.6%
 
,622781.1%
 

Victim Age
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct120
Distinct (%)< 0.1%
Missing312625
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean40.10168014
Minimum-9
Maximum934
Zeros82
Zeros (%)< 0.1%
Memory size5.7 MiB
2021-03-12T16:40:00.195104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile21
Q128
median37
Q350
95-th percentile68
Maximum934
Range943
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.27471157
Coefficient of variation (CV)0.3808995413
Kurtosis27.8020276
Mean40.10168014
Median Absolute Deviation (MAD)11
Skewness1.243897944
Sum17574040
Variance233.3168136
MonotocityNot monotonic
2021-03-12T16:40:00.347176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
26139291.9%
 
27138371.8%
 
28138111.8%
 
29135171.8%
 
25134461.8%
 
30133741.8%
 
31128241.7%
 
24125471.7%
 
32121621.6%
 
33117521.6%
 
23115171.5%
 
34114591.5%
 
35112291.5%
 
36104751.4%
 
22101611.4%
 
37101011.3%
 
3897791.3%
 
3995121.3%
 
4091981.2%
 
4186741.2%
 
2186221.1%
 
4281871.1%
 
4380911.1%
 
4578221.0%
 
4477461.0%
 
Other values (95)16446521.9%
 
(Missing)31262541.6%
 
ValueCountFrequency (%) 
-91< 0.1%
 
082< 0.1%
 
121< 0.1%
 
219< 0.1%
 
323< 0.1%
 
421< 0.1%
 
515< 0.1%
 
621< 0.1%
 
729< 0.1%
 
836< 0.1%
 
ValueCountFrequency (%) 
9341< 0.1%
 
4271< 0.1%
 
2261< 0.1%
 
1212< 0.1%
 
1204< 0.1%
 
11912< 0.1%
 
1184< 0.1%
 
1141< 0.1%
 
1121< 0.1%
 
1092< 0.1%
 

NIBRS Code
Categorical

HIGH CORRELATION
MISSING

Distinct50
Distinct (%)< 0.1%
Missing249799
Missing (%)33.3%
Memory size5.7 MiB
999
134246 
23F
50790 
290
43475 
240
39466 
220
37565 
Other values (45)
195521 
ValueCountFrequency (%) 
99913424617.9%
 
23F507906.8%
 
290434755.8%
 
240394665.3%
 
220375655.0%
 
23H329094.4%
 
120187312.5%
 
13B173572.3%
 
90Z169932.3%
 
90E156872.1%
 
23G154832.1%
 
13A136091.8%
 
35A121281.6%
 
13C118091.6%
 
23C79491.1%
 
90D53860.7%
 
90J45070.6%
 
52037540.5%
 
26A29780.4%
 
90F29290.4%
 
25025440.3%
 
27017240.2%
 
26B14310.2%
 
90C9630.1%
 
26F9500.1%
 
Other values (25)57000.8%
 
(Missing)24979933.3%
 
2021-03-12T16:40:00.529410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2021-03-12T16:40:00.691488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n49959822.2%
 
949327221.9%
 
230158513.4%
 
a24979911.1%
 
01971108.8%
 
31646557.3%
 
1616402.7%
 
F546692.4%
 
4394791.8%
 
H329211.5%
 
A296921.3%
 
C214551.0%
 
B196990.9%
 
5189550.8%
 
Z169930.8%
 
E159850.7%
 
G155100.7%
 
660930.3%
 
D60620.3%
 
J45070.2%
 
727370.1%
 
8170< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number128569657.1%
 
Lowercase Letter74939733.3%
 
Uppercase Letter2174939.7%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
949327238.4%
 
230158523.5%
 
019711015.3%
 
316465512.8%
 
1616404.8%
 
4394793.1%
 
5189551.5%
 
660930.5%
 
727370.2%
 
8170< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F5466925.1%
 
H3292115.1%
 
A2969213.7%
 
C214559.9%
 
B196999.1%
 
Z169937.8%
 
E159857.3%
 
G155107.1%
 
D60622.8%
 
J45072.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n49959866.7%
 
a24979933.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common128569657.1%
 
Latin96689042.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
949327238.4%
 
230158523.5%
 
019711015.3%
 
316465512.8%
 
1616404.8%
 
4394793.1%
 
5189551.5%
 
660930.5%
 
727370.2%
 
8170< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n49959851.7%
 
a24979925.8%
 
F546695.7%
 
H329213.4%
 
A296923.1%
 
C214552.2%
 
B196992.0%
 
Z169931.8%
 
E159851.7%
 
G155101.6%
 
D60620.6%
 
J45070.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2252586100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n49959822.2%
 
949327221.9%
 
230158513.4%
 
a24979911.1%
 
01971108.8%
 
31646557.3%
 
1616402.7%
 
F546692.4%
 
4394791.8%
 
H329211.5%
 
A296921.3%
 
C214551.0%
 
B196990.9%
 
5189550.8%
 
Z169930.8%
 
E159850.7%
 
G155100.7%
 
660930.3%
 
D60620.3%
 
J45070.2%
 
727370.1%
 
8170< 0.1%
 

Victim Age at Offense
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct109
Distinct (%)< 0.1%
Missing331921
Missing (%)44.2%
Infinite0
Infinite (%)0.0%
Mean39.92168826
Minimum0
Maximum934
Zeros25
Zeros (%)< 0.1%
Memory size5.7 MiB
2021-03-12T16:40:00.833699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q128
median37
Q350
95-th percentile68
Maximum934
Range934
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.31625651
Coefficient of variation (CV)0.3836575351
Kurtosis27.76084568
Mean39.92168826
Median Absolute Deviation (MAD)11
Skewness1.229593803
Sum16724832
Variance234.5877136
MonotocityNot monotonic
2021-03-12T16:40:01.011554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
26132961.8%
 
27131661.8%
 
25129621.7%
 
28127961.7%
 
29126191.7%
 
30123521.6%
 
24123371.6%
 
31120551.6%
 
32114291.5%
 
23113501.5%
 
33111681.5%
 
34107371.4%
 
35104201.4%
 
22100891.3%
 
3698091.3%
 
3795721.3%
 
3892651.2%
 
2188681.2%
 
3988481.2%
 
4086341.1%
 
4181191.1%
 
4277461.0%
 
4377361.0%
 
4574621.0%
 
2074581.0%
 
Other values (84)15864821.1%
 
(Missing)33192144.2%
 
ValueCountFrequency (%) 
025< 0.1%
 
11< 0.1%
 
72< 0.1%
 
91< 0.1%
 
101< 0.1%
 
131< 0.1%
 
141< 0.1%
 
151< 0.1%
 
165< 0.1%
 
1728420.4%
 
ValueCountFrequency (%) 
9341< 0.1%
 
1212< 0.1%
 
1204< 0.1%
 
11914< 0.1%
 
1186< 0.1%
 
1141< 0.1%
 
1121< 0.1%
 
1092< 0.1%
 
1081< 0.1%
 
1072< 0.1%
 

NIBRS Group
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing249799
Missing (%)33.3%
Memory size5.7 MiB
A
320329 
C
134246 
B
46488 
ValueCountFrequency (%) 
A32032942.7%
 
C13424617.9%
 
B464886.2%
 
(Missing)24979933.3%
 
2021-03-12T16:40:01.180910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:40:01.265681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:40:01.397755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.665365939
Min length1

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n49959840.0%
 
A32032925.6%
 
a24979920.0%
 
C13424610.7%
 
B464883.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter74939759.9%
 
Uppercase Letter50106340.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A32032963.9%
 
C13424626.8%
 
B464889.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n49959866.7%
 
a24979933.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1250460100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n49959840.0%
 
A32032925.6%
 
a24979920.0%
 
C13424610.7%
 
B464883.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1250460100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n49959840.0%
 
A32032925.6%
 
a24979920.0%
 
C13424610.7%
 
B464883.7%
 

NIBRS Type
Categorical

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)< 0.1%
Missing249799
Missing (%)33.3%
Memory size5.7 MiB
Coded
307051 
999 - No Coded
134246 
90Z - No Coded
 
16993
90E - No Coded
 
15687
No Coded
 
13205
Other values (8)
 
13881
ValueCountFrequency (%) 
Coded30705140.9%
 
999 - No Coded13424617.9%
 
90Z - No Coded169932.3%
 
90E - No Coded156872.1%
 
No Coded132051.8%
 
90D - No Coded53860.7%
 
90J - No Coded45070.6%
 
90F - No Coded29290.4%
 
90C - No Coded9630.1%
 
COD42< 0.1%
 
Not Coded31< 0.1%
 
90H - No Coded12< 0.1%
 
90G - No Coded11< 0.1%
 
(Missing)24979933.3%
 
2021-03-12T16:40:01.549664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-12T16:40:01.692547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length5
Mean length6.553764873
Min length3

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
d100204220.4%
 
o69499114.1%
 
55543811.3%
 
C50202610.2%
 
e50102110.2%
 
n49959810.2%
 
94492269.1%
 
a2497995.1%
 
N1939703.9%
 
-1807343.7%
 
0464880.9%
 
Z169930.3%
 
E156870.3%
 
D54280.1%
 
J45070.1%
 
F29290.1%
 
O42< 0.1%
 
t31< 0.1%
 
H12< 0.1%
 
G11< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter294748259.9%
 
Uppercase Letter74160515.1%
 
Space Separator55543811.3%
 
Decimal Number49571410.1%
 
Dash Punctuation1807343.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C50202667.7%
 
N19397026.2%
 
Z169932.3%
 
E156872.1%
 
D54280.7%
 
J45070.6%
 
F29290.4%
 
O42< 0.1%
 
H12< 0.1%
 
G11< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
d100204234.0%
 
o69499123.6%
 
e50102117.0%
 
n49959816.9%
 
a2497998.5%
 
t31< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
944922690.6%
 
0464889.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
555438100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-180734100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin368908775.0%
 
Common123188625.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
d100204227.2%
 
o69499118.8%
 
C50202613.6%
 
e50102113.6%
 
n49959813.5%
 
a2497996.8%
 
N1939705.3%
 
Z169930.5%
 
E156870.4%
 
D54280.1%
 
J45070.1%
 
F29290.1%
 
O42< 0.1%
 
t31< 0.1%
 
H12< 0.1%
 
G11< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
55543845.1%
 
944922636.5%
 
-18073414.7%
 
0464883.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4920973100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
d100204220.4%
 
o69499114.1%
 
55543811.3%
 
C50202610.2%
 
e50102110.2%
 
n49959810.2%
 
94492269.1%
 
a2497995.1%
 
N1939703.9%
 
-1807343.7%
 
0464880.9%
 
Z169930.3%
 
E156870.3%
 
D54280.1%
 
J45070.1%
 
F29290.1%
 
O42< 0.1%
 
t31< 0.1%
 
H12< 0.1%
 
G11< 0.1%
 

Victim Zip Code
Categorical

HIGH CARDINALITY
MISSING

Distinct8252
Distinct (%)1.2%
Missing52198
Missing (%)7.0%
Memory size5.7 MiB
75217
 
44682
75220
 
31346
75211
 
30324
75216
 
26402
75228
 
25721
Other values (8247)
540189 
ValueCountFrequency (%) 
75217446826.0%
 
75220313464.2%
 
75211303244.0%
 
75216264023.5%
 
75228257213.4%
 
75241254853.4%
 
75243246783.3%
 
75215222633.0%
 
75238218922.9%
 
75201217342.9%
 
75227197682.6%
 
75226174152.3%
 
75231165402.2%
 
75204156762.1%
 
75206132121.8%
 
75287130791.7%
 
75224124181.7%
 
75252122911.6%
 
75237120531.6%
 
75232107751.4%
 
75229106581.4%
 
75208102481.4%
 
75240102281.4%
 
7521293491.2%
 
7521993351.2%
 
Other values (8227)23109230.8%
 
(Missing)521987.0%
 
2021-03-12T16:40:01.896559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4195 ?
Unique (%)0.6%
2021-03-12T16:40:02.058510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length4.860945154
Min length1

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
780354422.0%
 
277828321.3%
 
574865820.5%
 
13301119.0%
 
02390616.5%
 
41657394.5%
 
31617734.4%
 
61109443.0%
 
81086423.0%
 
n1043962.9%
 
a521981.4%
 
9465421.3%
 
T3< 0.1%
 
X2< 0.1%
 
E1< 0.1%
 
A1< 0.1%
 
S1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number349329795.7%
 
Lowercase Letter1565944.3%
 
Uppercase Letter8< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n10439666.7%
 
a5219833.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
780354423.0%
 
277828322.3%
 
574865821.4%
 
13301119.4%
 
02390616.8%
 
41657394.7%
 
31617734.6%
 
61109443.2%
 
81086423.1%
 
9465421.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T337.5%
 
X225.0%
 
E112.5%
 
A112.5%
 
S112.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common349329795.7%
 
Latin1566024.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n10439666.7%
 
a5219833.3%
 
T3< 0.1%
 
X2< 0.1%
 
E1< 0.1%
 
A1< 0.1%
 
S1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
780354423.0%
 
277828322.3%
 
574865821.4%
 
13301119.4%
 
02390616.8%
 
41657394.7%
 
31617734.6%
 
61109443.2%
 
81086423.1%
 
9465421.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3649899100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
780354422.0%
 
277828321.3%
 
574865820.5%
 
13301119.0%
 
02390616.5%
 
41657394.5%
 
31617734.4%
 
61109443.0%
 
81086423.0%
 
n1043962.9%
 
a521981.4%
 
9465421.3%
 
T3< 0.1%
 
X2< 0.1%
 
E1< 0.1%
 
A1< 0.1%
 
S1< 0.1%
 

X Coordinate
Real number (ℝ≥0)

MISSING

Distinct264763
Distinct (%)36.2%
Missing19655
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean2494417.309
Minimum2411659.219
Maximum2593936.034
Zeros0
Zeros (%)0.0%
Memory size5.7 MiB
2021-03-12T16:40:02.391492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2411659.219
5-th percentile2461757.557
Q12478421.31
median2493141.174
Q32506986.702
95-th percentile2532534.595
Maximum2593936.034
Range182276.8152
Interquartile range (IQR)28565.39162

Descriptive statistics

Standard deviation21973.2709
Coefficient of variation (CV)0.008808979487
Kurtosis-0.3073381799
Mean2494417.309
Median Absolute Deviation (MAD)14255.12011
Skewness0.2900300986
Sum1.823935397e+12
Variance482824634.2
MonotocityNot monotonic
2021-03-12T16:40:02.544062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2492641.46814660.2%
 
2492348.12812230.2%
 
2499502.458650.1%
 
2475422.4227110.1%
 
2499425.9166890.1%
 
2475408.3646740.1%
 
2503951.5935560.1%
 
2521543.7544620.1%
 
2516304.5164600.1%
 
2499456.8464090.1%
 
2500935.3643900.1%
 
2489673.4283800.1%
 
2508871.9653760.1%
 
2483620.808372< 0.1%
 
2525177.503366< 0.1%
 
2492641.469362< 0.1%
 
2508871.965340< 0.1%
 
2461631.475335< 0.1%
 
2492371.732331< 0.1%
 
2499502.45323< 0.1%
 
2483568.792289< 0.1%
 
2483560.932288< 0.1%
 
2506329.165281< 0.1%
 
2499010.165278< 0.1%
 
2504450.932263< 0.1%
 
Other values (264738)71871895.7%
 
(Missing)196552.6%
 
ValueCountFrequency (%) 
2411659.2191< 0.1%
 
2413505.6111< 0.1%
 
2413571.3461< 0.1%
 
2417141.2441< 0.1%
 
2420879.1341< 0.1%
 
2421349.2861< 0.1%
 
2424063.0141< 0.1%
 
2424082.9691< 0.1%
 
2424822.821< 0.1%
 
2425995.5831< 0.1%
 
ValueCountFrequency (%) 
2593936.0341< 0.1%
 
2590761.3551< 0.1%
 
2590095.8331< 0.1%
 
2589825.4521< 0.1%
 
2587516.7881< 0.1%
 
2586045.0391< 0.1%
 
2586045.0071< 0.1%
 
2586002.8493< 0.1%
 
2581431.8791< 0.1%
 
2580575.4631< 0.1%
 

Y Cordinate
Real number (ℝ≥0)

MISSING

Distinct267203
Distinct (%)36.5%
Missing19655
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean6978244.473
Minimum6889043.424
Maximum7088136.937
Zeros0
Zeros (%)0.0%
Memory size5.7 MiB
2021-03-12T16:40:02.884852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum6889043.424
5-th percentile6927987.87
Q16954975.186
median6975775.919
Q37001639.3
95-th percentile7029817.774
Maximum7088136.937
Range199093.5131
Interquartile range (IQR)46664.11409

Descriptive statistics

Standard deviation31306.07023
Coefficient of variation (CV)0.004486238673
Kurtosis-0.5357662498
Mean6978244.473
Median Absolute Deviation (MAD)24022.30694
Skewness0.2793092357
Sum5.102541206e+12
Variance980070032.9
MonotocityNot monotonic
2021-03-12T16:40:03.047654image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6966533.81812230.2%
 
6966516.79910040.1%
 
7002995.8178650.1%
 
6966516.88240.1%
 
7002954.296890.1%
 
6993943.0046740.1%
 
6993986.5426220.1%
 
6970128.0695560.1%
 
7001551.0054600.1%
 
7003000.2294090.1%
 
6949914.5883900.1%
 
6970467.4633800.1%
 
7018275.8463760.1%
 
7025530.537372< 0.1%
 
6976100.025366< 0.1%
 
6950900.433340< 0.1%
 
7018275.846340< 0.1%
 
6966573.447331< 0.1%
 
7002995.817323< 0.1%
 
6936538.753289< 0.1%
 
6936539.928288< 0.1%
 
6926708.133281< 0.1%
 
6939430.423278< 0.1%
 
7000885.858263< 0.1%
 
6970467.463262< 0.1%
 
Other values (267178)71900295.8%
 
(Missing)196552.6%
 
ValueCountFrequency (%) 
6889043.4241< 0.1%
 
6889082.0161< 0.1%
 
6892806.9421< 0.1%
 
6894042.4721< 0.1%
 
6894112.2791< 0.1%
 
6894776.0161< 0.1%
 
6895387.8471< 0.1%
 
6898289.5292< 0.1%
 
6899909.5171< 0.1%
 
6900168.7091< 0.1%
 
ValueCountFrequency (%) 
7088136.9371< 0.1%
 
7083051.0852< 0.1%
 
7078364.0271< 0.1%
 
7077723.0761< 0.1%
 
7075280.0691< 0.1%
 
7067190.441< 0.1%
 
7066743.8861< 0.1%
 
7066222.3961< 0.1%
 
7062351.7711< 0.1%
 
7062220.9831< 0.1%
 

City
Categorical

HIGH CARDINALITY

Distinct128
Distinct (%)< 0.1%
Missing4934
Missing (%)0.7%
Memory size5.7 MiB
DALLAS
740696 
Dallas
 
3810
GARLAND
 
128
RICHARDSON
 
118
DAL
 
104
Other values (123)
 
1072
ValueCountFrequency (%) 
DALLAS74069698.6%
 
Dallas38100.5%
 
GARLAND128< 0.1%
 
RICHARDSON118< 0.1%
 
DAL104< 0.1%
 
MESQUITE86< 0.1%
 
DLS84< 0.1%
 
ROWLETT80< 0.1%
 
DUNCANVILLE75< 0.1%
 
CARROLLTON67< 0.1%
 
GRAND PRAIRIE61< 0.1%
 
DALLLAS56< 0.1%
 
DESOTO52< 0.1%
 
IRVING44< 0.1%
 
PLANO33< 0.1%
 
COPPELL33< 0.1%
 
ADDISON32< 0.1%
 
LANCASTER31< 0.1%
 
DALAS27< 0.1%
 
FARMERS BRANCH23< 0.1%
 
BALCH SPRINGS21< 0.1%
 
DALLS16< 0.1%
 
GRAND PRAI15< 0.1%
 
CEDAR HILL14< 0.1%
 
UNIVERSITY PARK13< 0.1%
 
Other values (103)209< 0.1%
 
(Missing)49340.7%
 
2021-03-12T16:40:03.249960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique68 ?
Unique (%)< 0.1%
2021-03-12T16:40:03.417270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length6
Mean length5.983721376
Min length1

Overview of Unicode Properties

Unique unicode characters38
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A148278933.0%
 
L148272133.0%
 
D74541116.6%
 
S74137916.5%
 
a125540.3%
 
n98680.2%
 
l76200.2%
 
s38100.1%
 
R1120< 0.1%
 
N844< 0.1%
 
I685< 0.1%
 
E669< 0.1%
 
O648< 0.1%
 
T485< 0.1%
 
C451< 0.1%
 
G312< 0.1%
 
H249< 0.1%
 
P238< 0.1%
 
U204< 0.1%
 
202< 0.1%
 
V161< 0.1%
 
M126< 0.1%
 
W108< 0.1%
 
Q89< 0.1%
 
B63< 0.1%
 
Other values (13)143< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter445888299.2%
 
Lowercase Letter338520.8%
 
Space Separator202< 0.1%
 
Decimal Number8< 0.1%
 
Other Punctuation5< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A148278933.3%
 
L148272133.3%
 
D74541116.7%
 
S74137916.6%
 
R1120< 0.1%
 
N844< 0.1%
 
I685< 0.1%
 
E669< 0.1%
 
O648< 0.1%
 
T485< 0.1%
 
C451< 0.1%
 
G312< 0.1%
 
H249< 0.1%
 
P238< 0.1%
 
U204< 0.1%
 
V161< 0.1%
 
M126< 0.1%
 
W108< 0.1%
 
Q89< 0.1%
 
B63< 0.1%
 
K52< 0.1%
 
F37< 0.1%
 
Y30< 0.1%
 
X10< 0.1%
 
J1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1255437.1%
 
n986829.2%
 
l762022.5%
 
s381011.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,480.0%
 
.120.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
202100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0225.0%
 
4225.0%
 
1112.5%
 
2112.5%
 
5112.5%
 
8112.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4492734> 99.9%
 
Common215< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A148278933.0%
 
L148272133.0%
 
D74541116.6%
 
S74137916.5%
 
a125540.3%
 
n98680.2%
 
l76200.2%
 
s38100.1%
 
R1120< 0.1%
 
N844< 0.1%
 
I685< 0.1%
 
E669< 0.1%
 
O648< 0.1%
 
T485< 0.1%
 
C451< 0.1%
 
G312< 0.1%
 
H249< 0.1%
 
P238< 0.1%
 
U204< 0.1%
 
V161< 0.1%
 
M126< 0.1%
 
W108< 0.1%
 
Q89< 0.1%
 
B63< 0.1%
 
K52< 0.1%
 
Other values (4)78< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
20294.0%
 
,41.9%
 
020.9%
 
420.9%
 
110.5%
 
210.5%
 
510.5%
 
.10.5%
 
810.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4492949100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A148278933.0%
 
L148272133.0%
 
D74541116.6%
 
S74137916.5%
 
a125540.3%
 
n98680.2%
 
l76200.2%
 
s38100.1%
 
R1120< 0.1%
 
N844< 0.1%
 
I685< 0.1%
 
E669< 0.1%
 
O648< 0.1%
 
T485< 0.1%
 
C451< 0.1%
 
G312< 0.1%
 
H249< 0.1%
 
P238< 0.1%
 
U204< 0.1%
 
202< 0.1%
 
V161< 0.1%
 
M126< 0.1%
 
W108< 0.1%
 
Q89< 0.1%
 
B63< 0.1%
 
Other values (13)143< 0.1%
 

State
Categorical

MISSING

Distinct36
Distinct (%)< 0.1%
Missing8971
Missing (%)1.2%
Memory size5.7 MiB
TX
741267 
T
 
434
TN
 
92
UT
 
26
TC
 
17
Other values (31)
 
55
ValueCountFrequency (%) 
TX74126798.7%
 
T4340.1%
 
TN92< 0.1%
 
UT26< 0.1%
 
TC17< 0.1%
 
DE9< 0.1%
 
UK3< 0.1%
 
X3< 0.1%
 
FL3< 0.1%
 
LA3< 0.1%
 
WA3< 0.1%
 
YX2< 0.1%
 
CA2< 0.1%
 
OR2< 0.1%
 
DC2< 0.1%
 
TY2< 0.1%
 
RX2< 0.1%
 
PA1< 0.1%
 
NY1< 0.1%
 
ST1< 0.1%
 
VA1< 0.1%
 
AZ1< 0.1%
 
AL1< 0.1%
 
TD1< 0.1%
 
WY1< 0.1%
 
Other values (11)11< 0.1%
 
(Missing)89711.2%
 
2021-03-12T16:40:03.602832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique19 ?
Unique (%)< 0.1%
2021-03-12T16:40:03.775486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.011365604
Min length1

Overview of Unicode Properties

Unique unicode characters25
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
T74184249.1%
 
X74127549.1%
 
n179421.2%
 
a89710.6%
 
N95< 0.1%
 
U29< 0.1%
 
C23< 0.1%
 
A14< 0.1%
 
D13< 0.1%
 
E9< 0.1%
 
L7< 0.1%
 
Y6< 0.1%
 
R5< 0.1%
 
W4< 0.1%
 
K4< 0.1%
 
S4< 0.1%
 
O3< 0.1%
 
F3< 0.1%
 
V2< 0.1%
 
I2< 0.1%
 
M1< 0.1%
 
J1< 0.1%
 
P1< 0.1%
 
G1< 0.1%
 
Z1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter148334598.2%
 
Lowercase Letter269131.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T74184250.0%
 
X74127550.0%
 
N95< 0.1%
 
U29< 0.1%
 
C23< 0.1%
 
A14< 0.1%
 
D13< 0.1%
 
E9< 0.1%
 
L7< 0.1%
 
Y6< 0.1%
 
R5< 0.1%
 
W4< 0.1%
 
K4< 0.1%
 
S4< 0.1%
 
O3< 0.1%
 
F3< 0.1%
 
V2< 0.1%
 
I2< 0.1%
 
M1< 0.1%
 
J1< 0.1%
 
P1< 0.1%
 
G1< 0.1%
 
Z1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n1794266.7%
 
a897133.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1510258100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
T74184249.1%
 
X74127549.1%
 
n179421.2%
 
a89710.6%
 
N95< 0.1%
 
U29< 0.1%
 
C23< 0.1%
 
A14< 0.1%
 
D13< 0.1%
 
E9< 0.1%
 
L7< 0.1%
 
Y6< 0.1%
 
R5< 0.1%
 
W4< 0.1%
 
K4< 0.1%
 
S4< 0.1%
 
O3< 0.1%
 
F3< 0.1%
 
V2< 0.1%
 
I2< 0.1%
 
M1< 0.1%
 
J1< 0.1%
 
P1< 0.1%
 
G1< 0.1%
 
Z1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1510258100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
T74184249.1%
 
X74127549.1%
 
n179421.2%
 
a89710.6%
 
N95< 0.1%
 
U29< 0.1%
 
C23< 0.1%
 
A14< 0.1%
 
D13< 0.1%
 
E9< 0.1%
 
L7< 0.1%
 
Y6< 0.1%
 
R5< 0.1%
 
W4< 0.1%
 
K4< 0.1%
 
S4< 0.1%
 
O3< 0.1%
 
F3< 0.1%
 
V2< 0.1%
 
I2< 0.1%
 
M1< 0.1%
 
J1< 0.1%
 
P1< 0.1%
 
G1< 0.1%
 
Z1< 0.1%
 

Location1
Categorical

HIGH CARDINALITY

Distinct181464
Distinct (%)24.3%
Missing3176
Missing (%)0.4%
Memory size5.7 MiB
1400 S LAMAR ST DALLAS, TX 75215 (32.767362, -96.795092)
 
3542
8687 N CENTRAL EXPY DALLAS, TX 75225 (32.86875, -96.770691)
 
1757
8008 HERB KELLEHER WAY DALLAS, TX 75235 (32.85262, -96.85281)
 
1540
1600 FUN WAY DALLAS, TX 75210
 
1062
725 N JIM MILLER RD DALLAS, TX 75217 (32.725715, -96.700115)
 
1049
Other values (181459)
738736 
ValueCountFrequency (%) 
1400 S LAMAR ST DALLAS, TX 75215 (32.767362, -96.795092)35420.5%
 
8687 N CENTRAL EXPY DALLAS, TX 75225 (32.86875, -96.770691)17570.2%
 
8008 HERB KELLEHER WAY DALLAS, TX 75235 (32.85262, -96.85281)15400.2%
 
1600 FUN WAY DALLAS, TX 7521010620.1%
 
725 N JIM MILLER RD DALLAS, TX 75217 (32.725715, -96.700115)10490.1%
 
8687 N CENTRAL SERV SB DALLAS, TX 75225 (32.86875, -96.770691)10120.1%
 
9915 E NORTHWEST HWY DALLAS, TX 75238 (32.863181, -96.715202)10080.1%
 
9301 FOREST LN DALLAS, TX 75243 (32.909205, -96.740013)8920.1%
 
1521 N COCKRELL HILL RD DALLAS, TX 75211 (32.763549, -96.895452)8650.1%
 
7401 SAMUELL BLVD DALLAS, TX 75228 (32.792531, -96.687213)8380.1%
 
3550 E OVERTON RD DALLAS, TX 75216 (32.721551, -96.768618)8030.1%
 
205 S LAMAR ST DALLAS, TX 75202 (32.7785, -96.803969)7910.1%
 
1999 E CAMP WISDOM RD DALLAS, TX 75241 (32.66308, -96.788319)7790.1%
 
200 SHORT BLVD DALLAS, TX 75232 (32.685839, -96.824371)7450.1%
 
7425 BONNIE VIEW RD DALLAS, TX 75241 (32.657999, -96.751032)7420.1%
 
9801 HARRY HINES BLVD DALLAS, TX 75220 (32.855094, -96.879573)7160.1%
 
13350 DALLAS PKWY DALLAS, TX 75240 (32.930161, -96.820963)7130.1%
 
1818 CORSICANA ST DALLAS, TX 75201 (32.776021, -96.791944)6820.1%
 
6185 RETAIL RD DALLAS, TX 75231 (32.861702, -96.754126)6420.1%
 
4230 W ILLINOIS AVE DALLAS, TX 75211 (32.720302, -96.889748)6290.1%
 
9350 SKILLMAN ST DALLAS, TX 75243 (32.904639, -96.712321)6050.1%
 
2417 N HASKELL AVE DALLAS, TX 75204 (32.804882, -96.789963)5850.1%
 
2755 E LEDBETTER DR DALLAS, TX 75216 (32.692737, -96.775909)5400.1%
 
334 S HALL ST DALLAS, TX 75226 (32.782588, -96.778435)5370.1%
 
555 S LAMAR ST DALLAS, TX 75202 (32.775343, -96.803021)5220.1%
 
Other values (181439)72409096.4%
 
(Missing)31760.4%
 
2021-03-12T16:40:04.865627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique103030 ?
Unique (%)13.8%
2021-03-12T16:40:05.088351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length81
Median length57
Mean length56.87666708
Min length3

Overview of Unicode Properties

Unique unicode characters52
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
41261919.7%
 
227239446.4%
 
723666275.5%
 
A21318975.0%
 
L21025154.9%
 
619238624.5%
 
318941394.4%
 
518292804.3%
 
918115064.2%
 
815725783.7%
 
115222513.6%
 
14670033.4%
 
,14629793.4%
 
.14424783.4%
 
014265493.3%
 
D12674083.0%
 
S12520112.9%
 
T12247062.9%
 
410906212.6%
 
R7983591.9%
 
E7860641.8%
 
X7638741.8%
 
-7211631.7%
 
(7210751.7%
 
)7210751.7%
 
Other values (27)35563738.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1816135742.5%
 
Uppercase Letter1385411932.4%
 
Space Separator41261919.7%
 
Other Punctuation29059676.8%
 
Control14670033.4%
 
Dash Punctuation7211631.7%
 
Open Punctuation7210751.7%
 
Close Punctuation7210751.7%
 
Lowercase Letter285780.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
2272394415.0%
 
7236662713.0%
 
6192386210.6%
 
3189413910.4%
 
5182928010.1%
 
9181150610.0%
 
815725788.7%
 
115222518.4%
 
014265497.9%
 
410906216.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
4126191100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A213189715.4%
 
L210251515.2%
 
D12674089.1%
 
S12520119.0%
 
T12247068.8%
 
R7983595.8%
 
E7860645.7%
 
X7638745.5%
 
N5826854.2%
 
O4250653.1%
 
I3462612.5%
 
V2612891.9%
 
W2303811.7%
 
M2196761.6%
 
C2178291.6%
 
H2068631.5%
 
B1865191.3%
 
Y1702841.2%
 
P1433341.0%
 
K1373851.0%
 
G1296430.9%
 
U1136750.8%
 
F1108530.8%
 
J342660.2%
 
Z94580.1%
 

Most frequent Control characters

ValueCountFrequency (%) 
1467003100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,146297950.3%
 
.144247849.6%
 
&459< 0.1%
 
/37< 0.1%
 
#9< 0.1%
 
'3< 0.1%
 
;2< 0.1%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(721075100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-721163100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)721075100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1079637.8%
 
l762026.7%
 
n635222.2%
 
s381013.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2882383167.5%
 
Latin1388269732.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
412619114.3%
 
227239449.5%
 
723666278.2%
 
619238626.7%
 
318941396.6%
 
518292806.3%
 
918115066.3%
 
815725785.5%
 
115222515.3%
 
14670035.1%
 
,14629795.1%
 
.14424785.0%
 
014265494.9%
 
410906213.8%
 
-7211632.5%
 
(7210752.5%
 
)7210752.5%
 
&459< 0.1%
 
/37< 0.1%
 
#9< 0.1%
 
'3< 0.1%
 
;2< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A213189715.4%
 
L210251515.1%
 
D12674089.1%
 
S12520119.0%
 
T12247068.8%
 
R7983595.8%
 
E7860645.7%
 
X7638745.5%
 
N5826854.2%
 
O4250653.1%
 
I3462612.5%
 
V2612891.9%
 
W2303811.7%
 
M2196761.6%
 
C2178291.6%
 
H2068631.5%
 
B1865191.3%
 
Y1702841.2%
 
P1433341.0%
 
K1373851.0%
 
G1296430.9%
 
U1136750.8%
 
F1108530.8%
 
J342660.2%
 
a107960.1%
 
Other values (5)290590.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII42706528100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
41261919.7%
 
227239446.4%
 
723666275.5%
 
A21318975.0%
 
L21025154.9%
 
619238624.5%
 
318941394.4%
 
518292804.3%
 
918115064.2%
 
815725783.7%
 
115222513.6%
 
14670033.4%
 
,14629793.4%
 
.14424783.4%
 
014265493.3%
 
D12674083.0%
 
S12520112.9%
 
T12247062.9%
 
410906212.6%
 
R7983591.9%
 
E7860641.8%
 
X7638741.8%
 
-7211631.7%
 
(7210751.7%
 
)7210751.7%
 
Other values (27)35563738.3%
 

Update Date
Date

Distinct687500
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
Minimum2014-06-01 12:57:58
Maximum2021-03-05 02:55:38
2021-03-12T16:40:05.267140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:40:05.414406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2021-03-12T16:35:43.237220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:43.710439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:44.148006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:45.641088image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:45.931669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:46.259747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:46.590013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:46.914920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:47.208471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:47.506914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:47.857964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:48.174963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:48.474714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:48.765714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:49.032713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:49.328710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:49.639269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:49.934272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:50.220271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:50.493275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:50.756268image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:51.024271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:51.288270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:51.591196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:51.872737image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:52.155737image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:52.424741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:52.711324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:53.015415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:53.289450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:53.549415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:53.805422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:54.193418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:54.497484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:54.806598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:55.094732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:55.356735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:55.629908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:55.885912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:56.140912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:56.420732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:56.680731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:56.946732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:57.192732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:57.465731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:57.721736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:58.012735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:58.404954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:58.780952image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:59.003627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:59.246622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:59.481837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:59.733147image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:35:59.985620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:00.217599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:00.500146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:00.743308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:01.017544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:01.320890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:01.664359image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:02.038909image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:02.341639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:02.662265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:02.894104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:03.163684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:03.395635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:03.665379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:03.944230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:04.231442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:04.679445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:04.945787image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:05.199908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:05.481229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:05.821553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:06.115554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:06.379495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:06.670435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:06.964144image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:07.249794image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:07.505888image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:07.775283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:08.038500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:08.302378image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:08.570375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:08.833774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:09.118392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:09.381462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:09.734722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:09.989585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:10.265888image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:10.616052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:11.006986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:11.552004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:11.964156image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:12.213021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:12.526022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:12.848024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:13.097879image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:13.375877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:13.620145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:13.908154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:14.222146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:14.540145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:14.872382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:15.232710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:15.485913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:15.768077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:16.084516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:16.392758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:16.658793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:17.052682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:17.310444image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:17.594626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:17.859540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:18.303692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:18.557596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:18.823493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:19.168850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:19.440085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:19.708766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:20.109131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:20.369990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:20.735126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:20.998305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:21.260540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:21.512735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:21.776748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:22.047198image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:22.410014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:22.673197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:22.956619image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:23.204761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:23.465078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:23.740975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:24.014770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:24.308765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:24.638339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:24.926662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:25.180103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:25.430174image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:25.698170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:25.951169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:26.211170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:26.472169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:26.749171image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:26.999170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:27.273422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:27.535422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:27.793422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:28.041620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:28.290623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:28.542621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:28.805620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:29.071621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:29.356344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:29.629132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:29.867616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:30.130747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:30.397479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:30.690845image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:30.952377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:31.237894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:31.484075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:31.776180image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:32.038749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-03-12T16:36:32.604790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:32.873790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-03-12T16:36:36.707955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:36.982119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:37.253112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:37.541281image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:37.800285image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:38.065456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:38.347491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:38.636461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:38.898454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:39.159459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:39.412459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-03-12T16:36:40.203590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:40.468590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:40.741591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:40.997739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:41.281738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-03-12T16:36:44.112514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-03-12T16:36:44.716914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:45.302458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-03-12T16:36:46.371984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:46.752981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:47.139273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:47.507275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:47.855273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:48.153273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:48.580276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:49.231866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:49.494869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:49.764871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:50.058866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:50.328869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:50.572872image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:50.834870image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:51.086866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:51.338871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:51.634871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:52.005871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:52.428576image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-03-12T16:36:53.107566image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-03-12T16:36:53.787709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:54.077656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:54.431641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:55.199209image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:55.760837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:56.036979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:56.306063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:56.544871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:56.819944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:57.054843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:57.359729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:57.636339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:57.895513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:58.116484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:58.383770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:58.640148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:58.906143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:59.158145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:59.419141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:59.705143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:36:59.984141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:00.271141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:00.554267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:00.820270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-03-12T16:37:01.332268image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-03-12T16:37:02.935461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-03-12T16:37:03.750628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:03.998628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-03-12T16:37:04.719630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:05.141634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:05.545636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:05.913630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:06.248633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:06.581630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:06.916630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:07.269629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:07.660758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:07.997767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:08.338760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:08.760437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:09.084436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:09.457983image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:09.871019image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:10.166982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:10.439189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:10.723190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-12T16:40:06.240565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-12T16:40:06.869358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-12T16:40:07.365339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-12T16:40:11.092942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-12T16:37:27.201636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:37:52.013863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:38:45.636789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-03-12T16:38:59.290854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

Incident AddressZip CodeType of IncidentModus Operandi (MO)Victim ConditionVictim Injury DescriptionVictim GenderPerson Involvement TypeIncident Number w/yearYear of IncidentService Number IDWatchCall (911) ProblemType LocationType of PropertyVictim TypeApartment NumberVictim RaceReporting AreaVictim Home AddressBeatVictim ApartmentDivisionSectorVictim CityCouncil DistrictVictim StateTarget Area Action GridsVictim Business NameCommunityVictim Business AddressVictim Business PhoneYear1 of OccurrenceMonth1 of OccurenceResponding Officer #1 Badge NoDay1 of the WeekResponding Officer #1 NameTime1 of OccurrenceResponding Officer #2 Badge NoDay1 of the YearResponding Officer #2 NameReporting Officer Badge NoYear2 of OccurrenceAssisting Officer Badge NoMonth2 of OccurenceReviewing Officer Badge NoDay2 of the WeekElement Number AssignedTime2 of OccurrenceInvestigating Unit 1Day2 of the YearInvestigating Unit 2Offense StatusUCR DispositionOffense Entered YearFamily OffenseOffense Entered MonthHate CrimeOffense Entered Day of the WeekHate Crime DescriptionOffense Entered TimeWeapon UsedOffense Entered Date/TimeGang Related OffenseVictim PackageCFS NumberDrug Related IstevencidentRMS CodeCriminal Justice Information Service CodePenal CodeUCR Offense NameSpecial Report (Pre-RMS)UCR Offense DescriptionUCR CodeVictim NameOffense TypeNIBRS CrimeNIBRS Crime CategoryVictim EthnicityNIBRS Crime AgainstVictim AgeNIBRS CodeVictim Age at OffenseNIBRS GroupNIBRS TypeVictim Zip CodeX CoordinateY CordinateCityStateLocation1Update Date
07740 MCCALLUM BLVD75252.0UNAUTHORIZED USE OF MOTOR VEH - TRUCK OR BUSSUSPECT TOOK COMP'S VEHICLE WITHOUT PERMISSION.NaNNaNNaNNaN069444-20202020069444-2020-01309 - THEFTApartment Parking LotNaNNaN127NaN6009.0NaN623.0NaNNORTH CENTRAL620.0NaND12NaNNaNNaNMcCallumCoit_PFANaNNaN2020March6157MonRUNDELL,ALFREDO16:30NaN83NaN61572020.08273March122184FriC69119:00Investigations87.0Special Investigations / Auto TheftSuspendedSuspended2020FalseAprilNaNFriNone15:11NaN108NaNNaN20-0692712NoFS-24110003-G1424110003PC 31.07NaNNaNNaNNaNNaNNaNUUMVMOTOR VEHICLE THEFTNaNPROPERTYNaN240NaNACodedNaN2.497997e+067.046427e+06DALLASTX7740 MCCALLUM BLVD\nDALLAS, TX 75252\n(32.987857, -96.772993)2020-12-09 11:18:34
13083 HERSCHEL AVE75219.0PUBLIC INTOXICATIONAP NATHALIE MONTERO WAS IN PUBLIC AND INTOXICATEDNaNNaNNaNVictim003862-20212021003862-2021-0116X - MAJOR DIST (VIOLENCE)Highway, Street, Alley ETCNaNGovernmentNaNNaN3115.09801 HARRY HINES BLVD544.0NaNNORTHWEST540.0DALLASD2TXWycliff LemmonNaNNaNNaNNaN2021January11984ThuMORRIS,TERRON02:3092127BETANCOURT,PAULO119842021.0NaNJanuary77397ThuA54202:45NaN7.0NaNClear by ArrestCBA (Over Age 17)2021FalseJanuaryNaNThuNone03:16NaN7NaNNaN21-0038891NoMC-99999999-NC31399999999PC 49.02NaNNaNNaNNaNCITY OF DALLASNaNALL OTHER OFFENSESALL OTHER OFFENSESNaNPERSON, PROPERTY, OR SOCIETYNaN90ZNaNB90Z - No Coded752202.485524e+066.983523e+06DALLASTX3083 HERSCHEL AVE\nDALLAS, TX 75219\n(32.814523, -96.816634)2021-01-07 06:11:11
28703 E R L THORNTON FWY75228.0UNAUTHORIZED USE OF MOTOR VEH - (ATT) TRUCK OR BUSSUSPECT DAMAGED VEHICLE DOOR AND INGNITIONNaNNaNNaNRegistered Owner172133-20202020172133-2020-011PSE/11V - BURG MOTOR VEHHotel/Motel/ETCMotor VehicleIndividualNaNNaN1204.01241 N GABBERT ST238.0NaNNORTHEAST230.0MONTICELLOD7ARBuckner 30NaNNaNNaNNaN2020September8202SatORTEGA,GUADALUPE21:00NaN270NaN82022020.010197September057074SunEX0105:00Investigations271.0Special Investigations / Auto TheftSuspendedSuspended2020FalseSeptemberNaNSunNone10:52NaN271NaNNaN20-1755351UNKMA-24110003-G1024110004PC 31.07NaNNaNNaNNaNCAMPOS, JUANNaNUUMVMOTOR VEHICLE THEFTNaNPROPERTYNaN240NaNACoded716552.523380e+066.977453e+06DALLASTX8703 E R L THORNTON FWY\nDALLAS, TX 75228\n(32.795842, -96.694137)2021-02-05 14:17:16
32551 ELM ST75226.0PUBLIC INTOXICATIONAP WAS INTOXICATED IN A PUBLIC PLACENaNNaNNaNVictim003828-20212021003828-2021-01340/01 - OTHERHotel/Motel/ETCNaNGovernment320NaN9202.0334 S HALL ST153.0NaNCENTRAL150.0DALLASD2TXMonument GoodLatimerNaNNaNNaNNaN2021January11994WedGONZALEZ,ADRIAN23:29103696MORGAN,HALEY119942021.0NaNJanuary120430WedE16423:30NaN6.0NaNClear by ArrestCBA (Over Age 17)2021FalseJanuaryNaNThuNone01:10NaN7NaNNaN21-0038232NoMC-99999999-NC31399999999PC 49.02NaNNaNNaNNaNCITY OF DALLASNaNALL OTHER OFFENSESALL OTHER OFFENSESNaNPERSON, PROPERTY, OR SOCIETYNaN90ZNaNB90Z - No Coded752262.494608e+066.972495e+06DALLASTX2551 ELM ST\nDALLAS, TX 75226\n(32.783788, -96.787823)2021-01-07 02:27:23
411327 REEDER RD75229.0INJURED PERSON- PUBLIC PROPERTY (OTHER THAN FIREARM) (NO OFFENSE)INJURED PERSON.NaNNaNMaleVictim003827-20212021003827-2021-0116XA - MAJOR DIST AMBULANCEBar/NightClub/DanceHall ETC.NaNIndividualNaNBlack3025.02503 JACKSON KELLER RD534.01306NORTHWEST530.0SAN ANTONIOD6TXNaNNaNNaNNaNNaN2021January10661ThuJARAMILLO,CARLOS00:00106677ROSS,TIMOTHY106612021.0NaNJanuary77397ThuA55200:10NaN7.0NaNSuspendedSuspended2021FalseJanuaryNaNThuNone01:09NaN7NaNNaN21-0038402NoNA-99999999-W199999999UCRNaNNaNNaNNaNPAYNE, MICHAEL, JAMESNaNMISCELLANEOUSMISCELLANEOUSNon-Hispanic or LatinoMISCELLANEOUS38.099938.0C999 - No Coded782302.460356e+067.011913e+06DALLASTX11327 REEDER RD\nDALLAS, TX 75229\n(32.894186, -96.895991)2021-01-07 10:48:41
511327 REEDER RD75229.0PUBLIC INTOXICATIONTHE SUSPECT WAS INTOXICATED IN PUBLIC.NaNNaNNaNVictim003837-20212021003837-2021-0116XA - MAJOR DIST AMBULANCEBar/NightClub/DanceHall ETC.NaNSociety/PublicNaNNaN3025.09801 HARRY HINES BLVD534.0NaNNORTHWEST530.0DALLASD6TXNaNNaNNaNNaNNaN2021January10661ThuJARAMILLO,CARLOS00:00106677ROSS,TIMOTHY106612021.0NaNJanuary77397ThuA55200:10NaN7.0NaNClear by ArrestCBA (Over Age 17)2021FalseJanuaryNaNThuNone01:28NaN7NaNNaN21-0038402NoMC-99999999-NC31399999999PC 49.02NaNNaNNaNNaNCITY OF DALLASNaNALL OTHER OFFENSESALL OTHER OFFENSESNaNPERSON, PROPERTY, OR SOCIETYNaN90ZNaNB90Z - No Coded752202.460356e+067.011913e+06DALLASTX11327 REEDER RD\nDALLAS, TX 75229\n(32.894186, -96.895991)2021-01-07 06:10:34
63019 BICKERS ST75212.0CRIM MISCHIEF > OR EQUAL $100 < $750SUSP THREW ROCK CAUSING DAMAGE TO WINDOWGoodNaNFemaleVictim083109-20202020083109-2020-0136X - MAJOR DIST (VIOLENCE)Apartment ResidenceNoneIndividual144White4011.03019 BICKERS ST422.0144SOUTHWEST420.0DALLASD6TXNaNNaNNaNNaNNaN2020May11131SatNIETO,DAVID16:0010735130RADFORD,JOSHUA111312020.07852May118185SatA75616:15Investigations130.0Property Crime Division / SW Property CrimesSuspendedSuspended2020FalseMayNaNSatNone17:00NaN130NaNNaN20-0835599NoMB-29990042-L9929990042PC 28.03(b)(2)NaNNaNNaNNaNMCROBERT, YVETTE, ANNENaNDESTRUCTION/ DAMAGE/ VANDALISM OF PROPERTYDESTRUCTION/ DAMAGE/ VANDALISM OF PROPERTYNon-Hispanic or LatinoPROPERTY45.029045.0ACoded752122.469542e+066.973501e+06DALLASTX3019 BICKERS ST\nDALLAS, TX 75212\n(32.787939, -96.869423)2020-11-13 11:20:48
713030 AUDELIA RD75243.0BMVUNKNOWN SUSP(S) TOOK THE COMP'S PROPERTY WITHOUT CONSENTNaNNaNMaleVictim191489-20202020191489-2020-013PSE/11V - BURG MOTOR VEHOutdoor Area Public/PrivateOutdoor Area Public/PrivateIndividualNaNAsian9605.013030 AUDELIA RD254.02129NORTHEAST250.0DALLASD10TXNaNNaNNaNNaNNaN2020October9018SunPHELPS,FARIE,DVON19:00NaN299NaN90182020.08219October122184MonEX0100:00Investigations300.0Property Crime Division / NE Property CrimesSuspendedSuspended2020FalseOctoberNaNMonNone12:48NaN300NaNNaN20-1947597NoMA-22990004-F122990004PC 30.04(a)NaNNaNNaNNaNKHADKA, SANTOSHNaNTHEFT FROM MOTOR VEHICLELARCENY/ THEFT OFFENSESNon-Hispanic or LatinoPROPERTY27.023F27.0ACoded752432.515449e+067.023836e+06DALLASTX13030 AUDELIA RD\nDALLAS, TX 75243\n(32.924254, -96.71798)2021-02-05 15:26:47
811819 BUSHMILLS RD75243.0UNAUTHORIZED USE OF MOTOR VEH - AUTOMOBILESUSPECT ENTERED AND TOOK THE COMP VEHICLE WITHOUT CONSENT. NFINaNNaNFemaleRegistered Owner117309-20192019117309-2019-01109V - UUMVHighway, Street, Alley ETCNaNIndividualNaNBlack9608.011819 BUSHMILLS RD258.0NaNNORTHEAST250.0DALLASD10TXNaNNaNNaNNaNNaN2019June9640MonWILLIS,RICKEY,DARAY19:30NaN161NaN96402019.0T270June120627TueB20908:30Investigations162.0Special Investigations / Auto TheftSuspendedSuspended2019FalseJuneNaNTueNone11:47NaN162NaNNaN19-1064313NoFS-24110003-G1324110003PC 31.07NaNNaNNaNNaNIBE, ANNE, UZOAMAKANaNUUMVMOTOR VEHICLE THEFTNon-Hispanic or LatinoPROPERTY53.024053.0ACoded752432.518314e+067.018713e+06DALLASTX11819 BUSHMILLS RD\nDALLAS, TX 75243\n(32.910094, -96.70801)2020-08-14 15:57:22
9910 TEXAS ST75204.0CRIM MISCHIEF > OR EQUAL $100 < $750SUSPECT SHOT AT A VEHICLE AND WAS RECKLESS IF IT WAS OCCUPPIEDNaNNaNNaNVictim081744-20202020081744-2020-022DASF-DIST ACTIVE SHOOTER FOOTHighway, Street, Alley ETCNaNBusinessNaNNaN2025.010155 MONROE DR154.0NaNCENTRAL150.0DALLASD14TXMonument GoodLatimerNaNNaNNaNNaN2020May11700ThuLONG,EMMET13:306931128LOPEZ JR,UBALDO117002020.09340May118918ThuD17213:31Investigations128.0Capers / AssaultsSuspendedSuspended2020FalseMayNaNThuNone14:07NaN128NaNNaN20-0820766NoMB-29990042-L9929990042PC 28.03(b)(2)NaNNaNNaNNaNUNITED POSTAL SERVICESNaNDESTRUCTION/ DAMAGE/ VANDALISM OF PROPERTYDESTRUCTION/ DAMAGE/ VANDALISM OF PROPERTYNaNPROPERTYNaN290NaNACoded752292.493731e+066.974992e+06DALLASTX910 TEXAS ST\nDALLAS, TX 75204\n(32.790681, -96.791106)2020-12-03 11:04:05

Last rows

Incident AddressZip CodeType of IncidentModus Operandi (MO)Victim ConditionVictim Injury DescriptionVictim GenderPerson Involvement TypeIncident Number w/yearYear of IncidentService Number IDWatchCall (911) ProblemType LocationType of PropertyVictim TypeApartment NumberVictim RaceReporting AreaVictim Home AddressBeatVictim ApartmentDivisionSectorVictim CityCouncil DistrictVictim StateTarget Area Action GridsVictim Business NameCommunityVictim Business AddressVictim Business PhoneYear1 of OccurrenceMonth1 of OccurenceResponding Officer #1 Badge NoDay1 of the WeekResponding Officer #1 NameTime1 of OccurrenceResponding Officer #2 Badge NoDay1 of the YearResponding Officer #2 NameReporting Officer Badge NoYear2 of OccurrenceAssisting Officer Badge NoMonth2 of OccurenceReviewing Officer Badge NoDay2 of the WeekElement Number AssignedTime2 of OccurrenceInvestigating Unit 1Day2 of the YearInvestigating Unit 2Offense StatusUCR DispositionOffense Entered YearFamily OffenseOffense Entered MonthHate CrimeOffense Entered Day of the WeekHate Crime DescriptionOffense Entered TimeWeapon UsedOffense Entered Date/TimeGang Related OffenseVictim PackageCFS NumberDrug Related IstevencidentRMS CodeCriminal Justice Information Service CodePenal CodeUCR Offense NameSpecial Report (Pre-RMS)UCR Offense DescriptionUCR CodeVictim NameOffense TypeNIBRS CrimeNIBRS Crime CategoryVictim EthnicityNIBRS Crime AgainstVictim AgeNIBRS CodeVictim Age at OffenseNIBRS GroupNIBRS TypeVictim Zip CodeX CoordinateY CordinateCityStateLocation1Update Date
75085218900 DALLAS PKWY75287.0ROBBERY OF BUSINESS (AGG)UNKNOWN SUSPECT/S, ROBBED RP AT GUNPOINT AND TOOK COMPS PROPERTY.NaNNaNNaNVictim241190-20182018241190-2018-01320 - ROBBERYConvenience StoreOtherBusiness100NaN4403.018900 DALLAS PKWY614.0100NORTH CENTRAL610.0DALLASD12TXNaNNaNNaNNaNNaN2018November10609MonGARRETT,LEQUITA22:3311271309RICHARDSON,MICHAEL,THOMAS106092018.08172November120627MonA64222:34Investigations309.0Capers / RobberySuspendedSuspended2018FalseNovemberNaNMonNone23:39Handgun309UNKNaN18-2019078NoF1-12990002-C312990002PC 29.03NaNNaNNaNNaNMY BEER STORE #2NaNROBBERY-BUSINESSROBBERYNaNPROPERTYNaN120NaNACoded752872.480929e+067.053144e+06DALLASTX18900 DALLAS PKWY\nDALLAS, TX 75287\n(33.006378, -96.8289)2018-11-16 02:43:26
7508535122 GASTON AVE75214.0FOUND PROPERTY (NO OFFENSE)FOUND PROPERTYNaNNaNNaNVictim291138-20162016291138-2016-0116X - MAJOR DIST (VIOLENCE)Highway, Street, Alley ETCNaNGovernment22ENaN1185.0334 S HALL114.0NaNCentral110.0DALLASNaNTXRoss BennettNaNNaNNaNNaN2016December10353WedHOPKINS,ZACHARY06:0210358342ALISCH,CHRISTOPHER103532016.08258December15356WedA15206:02Support342.0Support Division / Property RoomSuspendedSuspended2016FalseDecemberNaNWedNone07:30Other342UNKNaN16-2414189UNKNA-99999999-X399999999No OffenseFOUNDNaNFOUND PROPERTY4300.0CITY OF DALLASNOT CODEDNaNNaNNaNNaNNaNNaNNaNNaNNaN000002.501164e+066.979265e+06DALLASTX5122 GASTON AVE\nDALLAS, TX 75214\n(32.802753, -96.766133)2018-05-08 12:43:29
7508542425 VICTORY AVE75201.0BMVUNK SUSP BROKE INTO COMPS VEHICLE AND STOLE LISTED PROPERTYNaNNaNMaleVictim277650-20162016277650-2016-01111V - BURG MOTOR VEHApartment Parking LotNaNIndividual107White6065.0660 W 172ND PL131.0NaNCentral130.0BROOMFIELDNaNCONaNNaNNaNNaNNaN2016November9749SatULLAH,MICHAEL,RAHMAT17:305906324CASTILLO,XAVIER97492016.0NaNNovember105995SunL21207:00NaN325.0NaNSuspendedSuspended2016FalseNovemberNaNSunNone09:01Other325NoNaN16-2306180NoMA-22990004-F122990004PC 30.04(a)THEFT/BMVNaNTHEFT640.0ABBOTT, RICHARDPART1NaNNaNNon-Hispanic or LatinoNaN30.0NaN30.0NaNNaN800232.487576e+066.973646e+06DALLASTX2425 VICTORY AVE\nDALLAS, TX 75201\n(32.788253, -96.810745)2016-11-22 14:56:11
7508556161 TRAILGLEN DR75217.0ROBBERY OF INDIVIDUAL (AGG)UNK SUSPS TOOK PROPERTY FROM COMP AT GUN POINT.NaNNaNNaNVictim136953-20162016136953-2016-02119 - SHOOTINGParking Lot (Apartment)Parking LotBusinessNaNNaN2208.01306 WALLSTREET352.0NaNSouthEast350.0DALLAS8TXLoop12 JimMillerNaNNaNNaNNaN2016June10624TueARVIZU,JOSE00:2510543159BLEVINS,BRENNEN,DEAN106242016.07876June111210TueA31700:27Investigations159.0Capers / RobberySuspendedSuspended2016FalseJuneNaNTueNone00:46Unknown159UNKNaN16-1102826NoF1-12990002-C412990002PC 29.03ROBBERY-INDIVIDUALNaNROBBERY300.0COWBOY TAXIPART1NaNNaNNaNNaNNaNNaNNaNNaNNaN752012.517042e+066.947397e+06DALLASTX6161 TRAILGLEN DR\nDALLAS, TX 75217\n(32.713925, -96.715681)2016-06-14 10:59:25
75085614900 LASATER RD75253.0BMVUNKNOWN SUSP ENTERED UNLOCKED VEHICLE AND TOOK PROPERTYNaNNaNFemaleVictim177119-20172017177119-2017-02211V - BURG MOTOR VEHApartment ResidenceMotor VehicleIndividual333White6062.014900 LASATER RD357.0333SOUTHEAST350.0DALLASD8TXNaNNaNNaNNaNNaN2017August4665FriNEVILS,RODNEY,L09:20NaN216NaN46652017.0NaNAugust113327FriB32210:00NaN216.0NaNSuspendedSuspended2017FalseAugustNaNFriUnknown13:00Personal Weapons (Hands-Feet ETC)216UNKNaN17-1480983UNKMA-22990004-F122990004PC 30.04(a)THEFT/BMVNaNTHEFT640.0HALL, DELAYNAPART1THEFT FROM MOTOR VEHICLELARCENY/ THEFT OFFENSESNon-Hispanic or LatinoPROPERTY20.023F20.0ANo Coded752532.564223e+066.939842e+06DALLASTX14900 LASATER RD\nDALLAS, TX 75253\n(32.692324, -96.562298)2017-11-10 16:39:59
7508576508 LA GRANGE DR75241.0BURGLARY OF BUILDING - FORCED ENTRYUNK SUSP ENTERED LOCATION AND TOOK PROPERTY WITHOUT CONSENTNaNNaNMaleVictim173401-20172017173401-2017-01111R - BURG OF RESSingle Family Residence - VacantNoneIndividualNaNBlack4376.06909 BRANDFORD RD755.0NaNSOUTH CENTRAL750.0ROWLETTD8TXSimpson Stuart BonnieviewNaNNaNNaNNaN2017July10056SatORTIZ,MARCOS,JAVIER19:00NaN210NaN100562017.06464July113327SunC73814:00Investigations211.0Property Crime Division / SC Property CrimesSuspendedSuspended2017FalseJulyNaNSunNone21:37Other211NoNaN17-1450883NoFS-22990001-E122990001PC 30.02(c)(1)BURGLARY-BUSINESSNaNBURGLARY512.0HUNTER, CLIFTON, JAMESPART1BURGLARY-BUSINESSBURGLARY/ BREAKING & ENTERINGNon-Hispanic or LatinoPROPERTY31.022031.0ACoded750892.504783e+066.928854e+06DALLASTX6508 LA GRANGE DR\nDALLAS, TX 75241\n(32.66352, -96.757479)2017-08-23 10:44:57
7508581463 WAGON WHEELS TRL75241.0BMVUNK SUSP ENTERED COMP VEHICLE AND TOOK PROP W/O CONSENTNaNNaNMaleVictim295968-20162016295968-2016-02311V - BURG MOTOR VEHMotor VehicleNaNIndividualNaNBlack4336.0323 BRYANT LN735.0NaNSouth Central730.0CEDAR HILLNaNTXNaNNaNNaNNaNNaN2016December11132MonLEE,KEITH21:009870347SHAY,STEPHEN,CHARLES111322016.06795December36201MonA75421:30Investigations347.0Property Crime Division / SC Property CrimesSuspendedSuspended2016FalseDecemberNaNMonNone23:35Other347NoNaN16-2452130NoMA-22990004-F122990004PC 30.04(a)THEFT/BMVNaNTHEFT640.0PORTER, COURTNEYPART1NaNNaNNon-Hispanic or LatinoNaN20.0NaN20.0NaNNaN751042.491653e+066.935058e+06DALLASTX1463 WAGON WHEELS TRL\nDALLAS, TX 75241\n(32.681146, -96.79959)2016-12-19 14:57:36
7508593500 GREAT TRINITY FOREST WAY75216.0EVADING ARREST DETENTION W/PREV CONVICTION PC38.04(b1)EVADING ARREST W/ PREV CONVICTIONNaNNaNNaNVictim203277-20192019203277-2019-06355 - TRAFFIC STOPHighway, Street, Alley ETCNaNSociety/PublicNaNNaN4327.01400 S LAMAR ST736.0NaNSOUTH CENTRAL730.0DALLASD8TXNaNNaNNaNNaNNaN2019October10409SatMARQUEZ,ISRAEL23:58NaN278NaN104092019.0NaNOctober057074SatR3623:58NaN278.0NaNClear by ArrestCBA (Over Age 17)2019FalseOctoberNaNSunNone00:24NaN279NaNNaN19-1887132YesFS-48010019-U17948010019PC 38.04(b)(1)NaNNaNNaNNaNCITY OF DALLASNaNALL OTHER OFFENSESALL OTHER OFFENSESNaNPERSON, PROPERTY, OR SOCIETYNaN90ZNaNB90Z - No Coded752152.504766e+066.941550e+06DALLASTX3500 GREAT TRINITY FOREST WAY\nDALLAS, TX 75216\n(32.698633, -96.759207)2019-10-14 05:34:49
7508604901 MAPLE AVE75235.0PUBLIC INTOXICATIONSUSPECT WAS INTOXICATEDNaNNaNNaNVictim269196-20182018269196-2018-01315 - ASSIST OFFICERGrocery/SupermarketNaNSociety/PublicNaNNaN3113.09801 HARRY HINES BLVD541.0NaNNorthWest540.0DALLASNaNTXNaNNaNNaNNaNNaN2018December10667SunROSS,TIMOTHY23:4210661350JARAMILLO,CARLOS106672018.0NaNDecember123375SunA52623:42NaN350.0NaNClear by ArrestCBA (Over Age 17)2018FalseDecemberNaNMonNone00:31NaN351NaNNaN18-2271311NoMC-99999999-NC11299999999PC 49.02NaNNaNNaNNaNCITY OF DALLASNaNPUBLIC INTOXICATIONPUBLIC INTOXICATIONNaNSOCIETYNaN90ENaNB90E - No Coded752202.482083e+066.983492e+06DALLASTX4901 MAPLE AVE\nDALLAS, TX 75235\n(32.815007, -96.828581)2018-12-17 05:53:13
7508612909 FOREST LN75234.0THEFT OF PROP > OR EQUAL $2,500 <$30K (SHOPLIFT-NOT EMP) PC31.03 (e4A)CUT SECURITY LINES, STOLE PROPERTYNaNNaNNaNVictim054115-20182018054115-2018-01220 - ROBBERYSpecialty Store (In a Specific Item)NaNBusinessNaNNaN4550.02909 FOREST LN553.0NaNNORTHWEST550.0DALLASD6TXForest DennisNaNNaNNaNNaN2018March5777WedDESONIER,JOSEPH,A14:45NaN73NaN57772018.08956March105995WedB54214:47Investigations73.0Capers / RobberySuspendedSuspended2018FalseMarchNaNWedNone15:10NaN73NaNNaN18-0448774NoFS-23990194-F19523990194PC 31.03(e)(4)(A)THEFT/SHOPLIFTNaNTHEFT630.0SPRINT STOREPART1SHOPLIFTINGLARCENY/ THEFT OFFENSESNaNPROPERTYNaN23CNaNACoded752342.465562e+067.017959e+06DALLASTX2909 FOREST LN\nDALLAS, TX 75234\n(32.909487, -96.880157)2018-05-25 17:22:12